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haifengl/smile
math/src/main/java/smile/stat/distribution/GaussianDistribution.java
GaussianDistribution.randInverseCDF
public double randInverseCDF() { final double a0 = 2.50662823884; final double a1 = -18.61500062529; final double a2 = 41.39119773534; final double a3 = -25.44106049637; final double b0 = -8.47351093090; final double b1 = 23.08336743743; final double b2 = -21.06224101826; final double b3 = 3.13082909833; final double c0 = 0.3374754822726147; final double c1 = 0.9761690190917186; final double c2 = 0.1607979714918209; final double c3 = 0.0276438810333863; final double c4 = 0.0038405729373609; final double c5 = 0.0003951896511919; final double c6 = 0.0000321767881768; final double c7 = 0.0000002888167364; final double c8 = 0.0000003960315187; double y, r, x; double u = Math.random(); while (u == 0.0) { u = Math.random(); } y = u - 0.5; if (Math.abs(y) < 0.42) { r = y * y; x = y * (((a3 * r + a2) * r + a1) * r + a0) / ((((b3 * r + b2) * r + b1) * r + b0) * r + 1); } else { r = u; if (y > 0) { r = 1 - u; } r = Math.log(-Math.log(r)); x = c0 + r * (c1 + r * (c2 + r * (c3 + r * (c4 + r * (c5 + r * (c6 + r * (c7 + r * c8))))))); if (y < 0) { x = -(x); } } return mu + sigma * x; }
java
public double randInverseCDF() { final double a0 = 2.50662823884; final double a1 = -18.61500062529; final double a2 = 41.39119773534; final double a3 = -25.44106049637; final double b0 = -8.47351093090; final double b1 = 23.08336743743; final double b2 = -21.06224101826; final double b3 = 3.13082909833; final double c0 = 0.3374754822726147; final double c1 = 0.9761690190917186; final double c2 = 0.1607979714918209; final double c3 = 0.0276438810333863; final double c4 = 0.0038405729373609; final double c5 = 0.0003951896511919; final double c6 = 0.0000321767881768; final double c7 = 0.0000002888167364; final double c8 = 0.0000003960315187; double y, r, x; double u = Math.random(); while (u == 0.0) { u = Math.random(); } y = u - 0.5; if (Math.abs(y) < 0.42) { r = y * y; x = y * (((a3 * r + a2) * r + a1) * r + a0) / ((((b3 * r + b2) * r + b1) * r + b0) * r + 1); } else { r = u; if (y > 0) { r = 1 - u; } r = Math.log(-Math.log(r)); x = c0 + r * (c1 + r * (c2 + r * (c3 + r * (c4 + r * (c5 + r * (c6 + r * (c7 + r * c8))))))); if (y < 0) { x = -(x); } } return mu + sigma * x; }
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Uses Inverse CDF method to generate a Gaussian deviate.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/stat/distribution/GaussianDistribution.java#L172-L218
17,301
haifengl/smile
core/src/main/java/smile/classification/AdaBoost.java
AdaBoost.predict
@Override public int predict(double[] x, double[] posteriori) { Arrays.fill(posteriori, 0.0); for (int i = 0; i < trees.length; i++) { posteriori[trees[i].predict(x)] += alpha[i]; } double sum = Math.sum(posteriori); for (int i = 0; i < k; i++) { posteriori[i] /= sum; } return Math.whichMax(posteriori); }
java
@Override public int predict(double[] x, double[] posteriori) { Arrays.fill(posteriori, 0.0); for (int i = 0; i < trees.length; i++) { posteriori[trees[i].predict(x)] += alpha[i]; } double sum = Math.sum(posteriori); for (int i = 0; i < k; i++) { posteriori[i] /= sum; } return Math.whichMax(posteriori); }
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Predicts the class label of an instance and also calculate a posteriori probabilities. Not supported.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/classification/AdaBoost.java#L413-L427
17,302
haifengl/smile
math/src/main/java/smile/sort/IQAgent.java
IQAgent.quantile
public double quantile(double p) { if (nd > 0) { update(); } int jl = 0, jh = nq - 1, j; while (jh - jl > 1) { j = (jh + jl) >> 1; if (p > pval[j]) { jl = j; } else { jh = j; } } j = jl; double q = qile[j] + (qile[j + 1] - qile[j]) * (p - pval[j]) / (pval[j + 1] - pval[j]); return Math.max(qile[0], Math.min(qile[nq - 1], q)); }
java
public double quantile(double p) { if (nd > 0) { update(); } int jl = 0, jh = nq - 1, j; while (jh - jl > 1) { j = (jh + jl) >> 1; if (p > pval[j]) { jl = j; } else { jh = j; } } j = jl; double q = qile[j] + (qile[j + 1] - qile[j]) * (p - pval[j]) / (pval[j + 1] - pval[j]); return Math.max(qile[0], Math.min(qile[nq - 1], q)); }
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Returns the estimated p-quantile for the data seen so far. For example, p = 0.5 for median.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/sort/IQAgent.java#L149-L165
17,303
haifengl/smile
nlp/src/main/java/smile/nlp/Trie.java
Trie.put
public void put(K[] key, V value) { Node child = root.get(key[0]); if (child == null) { child = new Node(key[0]); root.put(key[0], child); } child.addChild(key, value, 1); }
java
public void put(K[] key, V value) { Node child = root.get(key[0]); if (child == null) { child = new Node(key[0]); root.put(key[0], child); } child.addChild(key, value, 1); }
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Add a key with associated value to the trie. @param key the key. @param value the value.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/nlp/src/main/java/smile/nlp/Trie.java#L136-L143
17,304
haifengl/smile
nlp/src/main/java/smile/nlp/Trie.java
Trie.get
public V get(K[] key) { Node child = root.get(key[0]); if (child != null) { return child.getChild(key, 1); } return null; }
java
public V get(K[] key) { Node child = root.get(key[0]); if (child != null) { return child.getChild(key, 1); } return null; }
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Returns the associated value of a given key. Returns null if the key doesn't exist in the trie. @param key the key. @return the associated value or null.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/nlp/src/main/java/smile/nlp/Trie.java#L151-L157
17,305
haifengl/smile
math/src/main/java/smile/math/matrix/LU.java
LU.inverse
public DenseMatrix inverse() { int m = lu.nrows(); int n = lu.ncols(); if (m != n) { throw new IllegalArgumentException(String.format("Matrix is not square: %d x %d", m, n)); } DenseMatrix inv = Matrix.zeros(n, n); for (int i = 0; i < n; i++) { inv.set(i, piv[i], 1.0); } solve(inv); return inv; }
java
public DenseMatrix inverse() { int m = lu.nrows(); int n = lu.ncols(); if (m != n) { throw new IllegalArgumentException(String.format("Matrix is not square: %d x %d", m, n)); } DenseMatrix inv = Matrix.zeros(n, n); for (int i = 0; i < n; i++) { inv.set(i, piv[i], 1.0); } solve(inv); return inv; }
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Returns the matrix inverse. For pseudo inverse, use QRDecomposition.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/LU.java#L118-L133
17,306
haifengl/smile
core/src/main/java/smile/clustering/GMeans.java
GMeans.AndersonDarling
private static double AndersonDarling(double[] x) { int n = x.length; Arrays.sort(x); for (int i = 0; i < n; i++) { x[i] = GaussianDistribution.getInstance().cdf(x[i]); // in case overflow when taking log later. if (x[i] == 0) x[i] = 0.0000001; if (x[i] == 1) x[i] = 0.9999999; } double A = 0.0; for (int i = 0; i < n; i++) { A -= (2*i+1) * (Math.log(x[i]) + Math.log(1-x[n-i-1])); } A = A / n - n; A *= (1 + 4.0/n - 25.0/(n*n)); return A; }
java
private static double AndersonDarling(double[] x) { int n = x.length; Arrays.sort(x); for (int i = 0; i < n; i++) { x[i] = GaussianDistribution.getInstance().cdf(x[i]); // in case overflow when taking log later. if (x[i] == 0) x[i] = 0.0000001; if (x[i] == 1) x[i] = 0.9999999; } double A = 0.0; for (int i = 0; i < n; i++) { A -= (2*i+1) * (Math.log(x[i]) + Math.log(1-x[n-i-1])); } A = A / n - n; A *= (1 + 4.0/n - 25.0/(n*n)); return A; }
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Calculates the Anderson-Darling statistic for one-dimensional normality test. @param x the samples to test if drawn from a Gaussian distribution.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/clustering/GMeans.java#L183-L203
17,307
haifengl/smile
demo/src/main/java/smile/demo/util/ParameterParser.java
ParameterParser.get
public String get(String name) { Parameter param = parameters.get(name); if (param != null) return param.value; else return null; }
java
public String get(String name) { Parameter param = parameters.get(name); if (param != null) return param.value; else return null; }
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Returns the value of a given parameter. Null if the parameter does not present.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/demo/src/main/java/smile/demo/util/ParameterParser.java#L121-L127
17,308
haifengl/smile
demo/src/main/java/smile/demo/util/ParameterParser.java
ParameterParser.filterMonadics
private List<String> filterMonadics(String[] args) {// name-value for monads List<String> filteredArgs = new ArrayList<>(); // Y <- return List for (String arg : args) { // iterate over args filteredArgs.add(arg); Parameter param = parameters.get(arg); if (param != null && param.paramType == ParameterType.MONADIC) { filteredArgs.add("1"); // insert a value to "1" } } return filteredArgs; // return List of args }
java
private List<String> filterMonadics(String[] args) {// name-value for monads List<String> filteredArgs = new ArrayList<>(); // Y <- return List for (String arg : args) { // iterate over args filteredArgs.add(arg); Parameter param = parameters.get(arg); if (param != null && param.paramType == ParameterType.MONADIC) { filteredArgs.add("1"); // insert a value to "1" } } return filteredArgs; // return List of args }
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Parses the args array looking for monads, arguments without value such as "-verbose" and if found inserts, forces, a value of "1" and returns the transformed args as a List @param args array of input values @return a list of expanded name-value pair if monads are detected
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/demo/src/main/java/smile/demo/util/ParameterParser.java#L137-L147
17,309
haifengl/smile
demo/src/main/java/smile/demo/util/ParameterParser.java
ParameterParser.parse
public List<String> parse(String[] args) { // merge args & defaults List<String> extras = new ArrayList<>(); List<String> filteredArgs = filterMonadics(args); // detect and fill mons for (int i = 0; i < filteredArgs.size(); i++) { String key = filteredArgs.get(i); if (key.equalsIgnoreCase("-h") || key.equalsIgnoreCase("-help")) { System.out.println(usage); System.exit(0); } if (parameters.containsKey(key)) { Parameter param = parameters.get(key); param.value = filteredArgs.get(i+1); switch(param.paramType) { case INTEGER: try { Integer.parseInt(param.value); } catch (Exception ex) { System.err.println("Invalid parameter " + param.name + ' ' + param.value); System.err.println(usage); System.exit(1); } break; case FLOAT: try { Double.parseDouble(param.value); } catch (Exception ex) { System.err.println("Invalid parameter " + param.name + ' ' + param.value); System.err.println(usage); System.exit(1); } break; default: // Just to remove unmatched case warning } i++; } else { extras.add(key); } } for(String key : parameters.keySet()) { Parameter param = parameters.get(key); if (param.mandatory && param.value == null) { System.err.println("Missing mandatory parameter: " + key); System.err.println(usage); System.exit(1); } } return extras; // parsed + defaults }
java
public List<String> parse(String[] args) { // merge args & defaults List<String> extras = new ArrayList<>(); List<String> filteredArgs = filterMonadics(args); // detect and fill mons for (int i = 0; i < filteredArgs.size(); i++) { String key = filteredArgs.get(i); if (key.equalsIgnoreCase("-h") || key.equalsIgnoreCase("-help")) { System.out.println(usage); System.exit(0); } if (parameters.containsKey(key)) { Parameter param = parameters.get(key); param.value = filteredArgs.get(i+1); switch(param.paramType) { case INTEGER: try { Integer.parseInt(param.value); } catch (Exception ex) { System.err.println("Invalid parameter " + param.name + ' ' + param.value); System.err.println(usage); System.exit(1); } break; case FLOAT: try { Double.parseDouble(param.value); } catch (Exception ex) { System.err.println("Invalid parameter " + param.name + ' ' + param.value); System.err.println(usage); System.exit(1); } break; default: // Just to remove unmatched case warning } i++; } else { extras.add(key); } } for(String key : parameters.keySet()) { Parameter param = parameters.get(key); if (param.mandatory && param.value == null) { System.err.println("Missing mandatory parameter: " + key); System.err.println(usage); System.exit(1); } } return extras; // parsed + defaults }
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Parse the arguments. @param args arguments @return a list of strings which are not parameters, e.g. a list of file name.
[ "Parse", "the", "arguments", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/demo/src/main/java/smile/demo/util/ParameterParser.java#L155-L208
17,310
haifengl/smile
core/src/main/java/smile/classification/RandomForest.java
RandomForest.getTrees
public DecisionTree[] getTrees() { DecisionTree[] forest = new DecisionTree[trees.size()]; for (int i = 0; i < forest.length; i++) forest[i] = trees.get(i).tree; return forest; }
java
public DecisionTree[] getTrees() { DecisionTree[] forest = new DecisionTree[trees.size()]; for (int i = 0; i < forest.length; i++) forest[i] = trees.get(i).tree; return forest; }
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Returns the decision trees.
[ "Returns", "the", "decision", "trees", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/classification/RandomForest.java#L859-L865
17,311
haifengl/smile
plot/src/main/java/smile/swing/table/ButtonCellRenderer.java
ButtonCellRenderer.setMnemonic
public void setMnemonic(int mnemonic) { this.mnemonic = mnemonic; renderButton.setMnemonic(mnemonic); editButton.setMnemonic(mnemonic); }
java
public void setMnemonic(int mnemonic) { this.mnemonic = mnemonic; renderButton.setMnemonic(mnemonic); editButton.setMnemonic(mnemonic); }
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The mnemonic to activate the button when the cell has focus @param mnemonic the mnemonic
[ "The", "mnemonic", "to", "activate", "the", "button", "when", "the", "cell", "has", "focus" ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/swing/table/ButtonCellRenderer.java#L122-L126
17,312
haifengl/smile
core/src/main/java/smile/association/TotalSupportTree.java
TotalSupportTree.add
private void add(Node node, int size, int index, int[] itemset, int support) { if (node.children == null) { node.children = new Node[size]; } int item = order[itemset[index]]; if (node.children[item] == null) { node.children[item] = new Node(itemset[index]); } if (index == 0) { node.children[item].support += support; } else { add(node.children[item], item, index - 1, itemset, support); } }
java
private void add(Node node, int size, int index, int[] itemset, int support) { if (node.children == null) { node.children = new Node[size]; } int item = order[itemset[index]]; if (node.children[item] == null) { node.children[item] = new Node(itemset[index]); } if (index == 0) { node.children[item].support += support; } else { add(node.children[item], item, index - 1, itemset, support); } }
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Inserts a node into a T-tree. @param node the root of subtree. @param size the size of the current array in T-tree. @param index the index of the last item in the item set, which is also used as a level counter. @param itemset the given item set. @param support the support value associated with the given item set.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/association/TotalSupportTree.java#L108-L123
17,313
haifengl/smile
core/src/main/java/smile/association/TotalSupportTree.java
TotalSupportTree.getSupport
public int getSupport(int[] itemset) { if (root.children != null) { return getSupport(itemset, itemset.length - 1, root); } else { return 0; } }
java
public int getSupport(int[] itemset) { if (root.children != null) { return getSupport(itemset, itemset.length - 1, root); } else { return 0; } }
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Returns the support value for the given item set. @param itemset the given item set. The items in the set has to be in the descending order according to their frequency. @return the support value (0 if not found)
[ "Returns", "the", "support", "value", "for", "the", "given", "item", "set", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/association/TotalSupportTree.java#L131-L137
17,314
haifengl/smile
core/src/main/java/smile/association/TotalSupportTree.java
TotalSupportTree.getSupport
private int getSupport(int[] itemset, int index, Node node) { int item = order[itemset[index]]; Node child = node.children[item]; if (child != null) { // If the index is 0, then this is the last element (i.e the // input is a 1 itemset) and therefore item set found if (index == 0) { return child.support; } else { if (child.children != null) { return getSupport(itemset, index - 1, child); } } } return 0; }
java
private int getSupport(int[] itemset, int index, Node node) { int item = order[itemset[index]]; Node child = node.children[item]; if (child != null) { // If the index is 0, then this is the last element (i.e the // input is a 1 itemset) and therefore item set found if (index == 0) { return child.support; } else { if (child.children != null) { return getSupport(itemset, index - 1, child); } } } return 0; }
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Returns the support value for the given item set if found in the T-tree and 0 otherwise. @param itemset the given item set. @param index the current index in the given item set. @param nodes the nodes of the current T-tree level. @return the support value (0 if not found)
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/association/TotalSupportTree.java#L147-L163
17,315
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.initTokenizer
private void initTokenizer(StreamTokenizer tokenizer) { tokenizer.resetSyntax(); tokenizer.whitespaceChars(0, ' '); tokenizer.wordChars(' ' + 1, '\u00FF'); tokenizer.whitespaceChars(',', ','); tokenizer.commentChar('%'); tokenizer.quoteChar('"'); tokenizer.quoteChar('\''); tokenizer.ordinaryChar('{'); tokenizer.ordinaryChar('}'); tokenizer.eolIsSignificant(true); }
java
private void initTokenizer(StreamTokenizer tokenizer) { tokenizer.resetSyntax(); tokenizer.whitespaceChars(0, ' '); tokenizer.wordChars(' ' + 1, '\u00FF'); tokenizer.whitespaceChars(',', ','); tokenizer.commentChar('%'); tokenizer.quoteChar('"'); tokenizer.quoteChar('\''); tokenizer.ordinaryChar('{'); tokenizer.ordinaryChar('}'); tokenizer.eolIsSignificant(true); }
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Initializes the StreamTokenizer used for reading the ARFF file.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L118-L129
17,316
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.getFirstToken
private void getFirstToken(StreamTokenizer tokenizer) throws IOException { while (tokenizer.nextToken() == StreamTokenizer.TT_EOL) { } if ((tokenizer.ttype == '\'') || (tokenizer.ttype == '"')) { tokenizer.ttype = StreamTokenizer.TT_WORD; } else if ((tokenizer.ttype == StreamTokenizer.TT_WORD) && (tokenizer.sval.equals("?"))) { tokenizer.ttype = '?'; } }
java
private void getFirstToken(StreamTokenizer tokenizer) throws IOException { while (tokenizer.nextToken() == StreamTokenizer.TT_EOL) { } if ((tokenizer.ttype == '\'') || (tokenizer.ttype == '"')) { tokenizer.ttype = StreamTokenizer.TT_WORD; } else if ((tokenizer.ttype == StreamTokenizer.TT_WORD) && (tokenizer.sval.equals("?"))) { tokenizer.ttype = '?'; } }
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Gets next token, skipping empty lines. @throws IOException if reading the next token fails
[ "Gets", "next", "token", "skipping", "empty", "lines", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L136-L145
17,317
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.getLastToken
private void getLastToken(StreamTokenizer tokenizer, boolean endOfFileOk) throws IOException, ParseException { if ((tokenizer.nextToken() != StreamTokenizer.TT_EOL) && ((tokenizer.ttype != StreamTokenizer.TT_EOF) || !endOfFileOk)) { throw new ParseException("end of line expected", tokenizer.lineno()); } }
java
private void getLastToken(StreamTokenizer tokenizer, boolean endOfFileOk) throws IOException, ParseException { if ((tokenizer.nextToken() != StreamTokenizer.TT_EOL) && ((tokenizer.ttype != StreamTokenizer.TT_EOF) || !endOfFileOk)) { throw new ParseException("end of line expected", tokenizer.lineno()); } }
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Gets token and checks if it's end of line. @param endOfFileOk true if EOF is OK @throws IllegalStateException if it doesn't find an end of line
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L153-L157
17,318
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.getNextToken
private void getNextToken(StreamTokenizer tokenizer) throws IOException, ParseException { if (tokenizer.nextToken() == StreamTokenizer.TT_EOL) { throw new ParseException("premature end of line", tokenizer.lineno()); } if (tokenizer.ttype == StreamTokenizer.TT_EOF) { throw new ParseException(PREMATURE_END_OF_FILE, tokenizer.lineno()); } else if ((tokenizer.ttype == '\'') || (tokenizer.ttype == '"')) { tokenizer.ttype = StreamTokenizer.TT_WORD; } else if ((tokenizer.ttype == StreamTokenizer.TT_WORD) && (tokenizer.sval.equals("?"))) { tokenizer.ttype = '?'; } }
java
private void getNextToken(StreamTokenizer tokenizer) throws IOException, ParseException { if (tokenizer.nextToken() == StreamTokenizer.TT_EOL) { throw new ParseException("premature end of line", tokenizer.lineno()); } if (tokenizer.ttype == StreamTokenizer.TT_EOF) { throw new ParseException(PREMATURE_END_OF_FILE, tokenizer.lineno()); } else if ((tokenizer.ttype == '\'') || (tokenizer.ttype == '"')) { tokenizer.ttype = StreamTokenizer.TT_WORD; } else if ((tokenizer.ttype == StreamTokenizer.TT_WORD) && (tokenizer.sval.equals("?"))) { tokenizer.ttype = '?'; } }
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Gets next token, checking for a premature and of line. @throws IllegalStateException if it finds a premature end of line
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L164-L175
17,319
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.readHeader
private String readHeader(StreamTokenizer tokenizer, List<Attribute> attributes) throws IOException, ParseException { /// The name of dataset. String relationName = null; // clear attribute set, which may be from previous parsing of other datasets. attributes.clear(); // Get name of relation. getFirstToken(tokenizer); if (tokenizer.ttype == StreamTokenizer.TT_EOF) { throw new ParseException(PREMATURE_END_OF_FILE, tokenizer.lineno()); } if (ARFF_RELATION.equalsIgnoreCase(tokenizer.sval)) { getNextToken(tokenizer); relationName = tokenizer.sval; getLastToken(tokenizer, false); } else { throw new ParseException("keyword " + ARFF_RELATION + " expected", tokenizer.lineno()); } // Get attribute declarations. getFirstToken(tokenizer); if (tokenizer.ttype == StreamTokenizer.TT_EOF) { throw new ParseException(PREMATURE_END_OF_FILE, tokenizer.lineno()); } while (ARFF_ATTRIBUTE.equalsIgnoreCase(tokenizer.sval)) { attributes.add(parseAttribute(tokenizer)); } // Check if data part follows. We can't easily check for EOL. if (!ARFF_DATA.equalsIgnoreCase(tokenizer.sval)) { throw new ParseException("keyword " + ARFF_DATA + " expected", tokenizer.lineno()); } // Check if any attributes have been declared. if (attributes.isEmpty()) { throw new ParseException("no attributes declared", tokenizer.lineno()); } if (responseIndex >= attributes.size()) { throw new ParseException("Invalid response variable index", responseIndex); } return relationName; }
java
private String readHeader(StreamTokenizer tokenizer, List<Attribute> attributes) throws IOException, ParseException { /// The name of dataset. String relationName = null; // clear attribute set, which may be from previous parsing of other datasets. attributes.clear(); // Get name of relation. getFirstToken(tokenizer); if (tokenizer.ttype == StreamTokenizer.TT_EOF) { throw new ParseException(PREMATURE_END_OF_FILE, tokenizer.lineno()); } if (ARFF_RELATION.equalsIgnoreCase(tokenizer.sval)) { getNextToken(tokenizer); relationName = tokenizer.sval; getLastToken(tokenizer, false); } else { throw new ParseException("keyword " + ARFF_RELATION + " expected", tokenizer.lineno()); } // Get attribute declarations. getFirstToken(tokenizer); if (tokenizer.ttype == StreamTokenizer.TT_EOF) { throw new ParseException(PREMATURE_END_OF_FILE, tokenizer.lineno()); } while (ARFF_ATTRIBUTE.equalsIgnoreCase(tokenizer.sval)) { attributes.add(parseAttribute(tokenizer)); } // Check if data part follows. We can't easily check for EOL. if (!ARFF_DATA.equalsIgnoreCase(tokenizer.sval)) { throw new ParseException("keyword " + ARFF_DATA + " expected", tokenizer.lineno()); } // Check if any attributes have been declared. if (attributes.isEmpty()) { throw new ParseException("no attributes declared", tokenizer.lineno()); } if (responseIndex >= attributes.size()) { throw new ParseException("Invalid response variable index", responseIndex); } return relationName; }
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Reads and stores header of an ARFF file. @param attributes the set of attributes in this relation. @return the name of relation. @throws IllegalStateException if the information is not read successfully
[ "Reads", "and", "stores", "header", "of", "an", "ARFF", "file", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L184-L228
17,320
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.parseAttribute
private Attribute parseAttribute(StreamTokenizer tokenizer) throws IOException, ParseException { Attribute attribute = null; // Get attribute name. getNextToken(tokenizer); String attributeName = tokenizer.sval; getNextToken(tokenizer); // Check if attribute is nominal. if (tokenizer.ttype == StreamTokenizer.TT_WORD) { // Attribute is real, integer, or string. if (tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_REAL) || tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_INTEGER) || tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_NUMERIC)) { attribute = new NumericAttribute(attributeName); readTillEOL(tokenizer); } else if (tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_STRING)) { attribute = new StringAttribute(attributeName); readTillEOL(tokenizer); } else if (tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_DATE)) { String format = null; if (tokenizer.nextToken() != StreamTokenizer.TT_EOL) { if ((tokenizer.ttype != StreamTokenizer.TT_WORD) && (tokenizer.ttype != '\'') && (tokenizer.ttype != '\"')) { throw new ParseException("not a valid date format", tokenizer.lineno()); } format = tokenizer.sval; readTillEOL(tokenizer); } else { tokenizer.pushBack(); } attribute = new DateAttribute(attributeName, null, format); readTillEOL(tokenizer); } else if (tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_RELATIONAL)) { readTillEOL(tokenizer); } else if (tokenizer.sval.equalsIgnoreCase(ARFF_END_SUBRELATION)) { getNextToken(tokenizer); } else { throw new ParseException("Invalid attribute type or invalid enumeration", tokenizer.lineno()); } } else { // Attribute is nominal. List<String> attributeValues = new ArrayList<>(); tokenizer.pushBack(); // Get values for nominal attribute. if (tokenizer.nextToken() != '{') { throw new ParseException("{ expected at beginning of enumeration", tokenizer.lineno()); } while (tokenizer.nextToken() != '}') { if (tokenizer.ttype == StreamTokenizer.TT_EOL) { throw new ParseException("} expected at end of enumeration", tokenizer.lineno()); } else { attributeValues.add(tokenizer.sval.trim()); } } String[] values = new String[attributeValues.size()]; for (int i = 0; i < values.length; i++) { values[i] = attributeValues.get(i); } attribute = new NominalAttribute(attributeName, values); } getLastToken(tokenizer, false); getFirstToken(tokenizer); if (tokenizer.ttype == StreamTokenizer.TT_EOF) { throw new ParseException(PREMATURE_END_OF_FILE, tokenizer.lineno()); } return attribute; }
java
private Attribute parseAttribute(StreamTokenizer tokenizer) throws IOException, ParseException { Attribute attribute = null; // Get attribute name. getNextToken(tokenizer); String attributeName = tokenizer.sval; getNextToken(tokenizer); // Check if attribute is nominal. if (tokenizer.ttype == StreamTokenizer.TT_WORD) { // Attribute is real, integer, or string. if (tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_REAL) || tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_INTEGER) || tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_NUMERIC)) { attribute = new NumericAttribute(attributeName); readTillEOL(tokenizer); } else if (tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_STRING)) { attribute = new StringAttribute(attributeName); readTillEOL(tokenizer); } else if (tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_DATE)) { String format = null; if (tokenizer.nextToken() != StreamTokenizer.TT_EOL) { if ((tokenizer.ttype != StreamTokenizer.TT_WORD) && (tokenizer.ttype != '\'') && (tokenizer.ttype != '\"')) { throw new ParseException("not a valid date format", tokenizer.lineno()); } format = tokenizer.sval; readTillEOL(tokenizer); } else { tokenizer.pushBack(); } attribute = new DateAttribute(attributeName, null, format); readTillEOL(tokenizer); } else if (tokenizer.sval.equalsIgnoreCase(ARFF_ATTRIBUTE_RELATIONAL)) { readTillEOL(tokenizer); } else if (tokenizer.sval.equalsIgnoreCase(ARFF_END_SUBRELATION)) { getNextToken(tokenizer); } else { throw new ParseException("Invalid attribute type or invalid enumeration", tokenizer.lineno()); } } else { // Attribute is nominal. List<String> attributeValues = new ArrayList<>(); tokenizer.pushBack(); // Get values for nominal attribute. if (tokenizer.nextToken() != '{') { throw new ParseException("{ expected at beginning of enumeration", tokenizer.lineno()); } while (tokenizer.nextToken() != '}') { if (tokenizer.ttype == StreamTokenizer.TT_EOL) { throw new ParseException("} expected at end of enumeration", tokenizer.lineno()); } else { attributeValues.add(tokenizer.sval.trim()); } } String[] values = new String[attributeValues.size()]; for (int i = 0; i < values.length; i++) { values[i] = attributeValues.get(i); } attribute = new NominalAttribute(attributeName, values); } getLastToken(tokenizer, false); getFirstToken(tokenizer); if (tokenizer.ttype == StreamTokenizer.TT_EOF) { throw new ParseException(PREMATURE_END_OF_FILE, tokenizer.lineno()); } return attribute; }
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Parses the attribute declaration. @return an attributes in this relation @throws IOException if the information is not read successfully
[ "Parses", "the", "attribute", "declaration", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L237-L314
17,321
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.readTillEOL
private void readTillEOL(StreamTokenizer tokenizer) throws IOException { while (tokenizer.nextToken() != StreamTokenizer.TT_EOL) { } tokenizer.pushBack(); }
java
private void readTillEOL(StreamTokenizer tokenizer) throws IOException { while (tokenizer.nextToken() != StreamTokenizer.TT_EOL) { } tokenizer.pushBack(); }
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Reads and skips all tokens before next end of line token. @throws IOException in case something goes wrong
[ "Reads", "and", "skips", "all", "tokens", "before", "next", "end", "of", "line", "token", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L321-L326
17,322
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.getAttributes
public static Attribute[] getAttributes(InputStream stream) throws IOException, ParseException { Reader r = new BufferedReader(new InputStreamReader(stream)); StreamTokenizer tokenizer = new StreamTokenizer(r); ArffParser parser = new ArffParser(); parser.initTokenizer(tokenizer); List<Attribute> attributes = new ArrayList<>(); parser.readHeader(tokenizer, attributes); return attributes.toArray(new Attribute[attributes.size()]); }
java
public static Attribute[] getAttributes(InputStream stream) throws IOException, ParseException { Reader r = new BufferedReader(new InputStreamReader(stream)); StreamTokenizer tokenizer = new StreamTokenizer(r); ArffParser parser = new ArffParser(); parser.initTokenizer(tokenizer); List<Attribute> attributes = new ArrayList<>(); parser.readHeader(tokenizer, attributes); return attributes.toArray(new Attribute[attributes.size()]); }
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Returns the attribute set of given stream.
[ "Returns", "the", "attribute", "set", "of", "given", "stream", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L355-L366
17,323
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.parse
public AttributeDataset parse(InputStream stream) throws IOException, ParseException { try (Reader r = new BufferedReader(new InputStreamReader(stream))) { StreamTokenizer tokenizer = new StreamTokenizer(r); initTokenizer(tokenizer); List<Attribute> attributes = new ArrayList<>(); String relationName = readHeader(tokenizer, attributes); if (attributes.isEmpty()) { throw new IOException("no header information available"); } Attribute response = null; Attribute[] attr = new Attribute[attributes.size()]; attributes.toArray(attr); for (int i = 0; i < attributes.size(); i++) { if (responseIndex == i) { response = attributes.remove(i); break; } } AttributeDataset data = new AttributeDataset(relationName, attributes.toArray(new Attribute[attributes.size()]), response); while (true) { // Check if end of file reached. getFirstToken(tokenizer); if (tokenizer.ttype == StreamTokenizer.TT_EOF) { break; } // Parse instance if (tokenizer.ttype == '{') { readSparseInstance(tokenizer, data, attr); } else { readInstance(tokenizer, data, attr); } } for (Attribute attribute : attributes) { if (attribute instanceof NominalAttribute) { NominalAttribute a = (NominalAttribute) attribute; a.setOpen(false); } if (attribute instanceof StringAttribute) { StringAttribute a = (StringAttribute) attribute; a.setOpen(false); } } return data; } }
java
public AttributeDataset parse(InputStream stream) throws IOException, ParseException { try (Reader r = new BufferedReader(new InputStreamReader(stream))) { StreamTokenizer tokenizer = new StreamTokenizer(r); initTokenizer(tokenizer); List<Attribute> attributes = new ArrayList<>(); String relationName = readHeader(tokenizer, attributes); if (attributes.isEmpty()) { throw new IOException("no header information available"); } Attribute response = null; Attribute[] attr = new Attribute[attributes.size()]; attributes.toArray(attr); for (int i = 0; i < attributes.size(); i++) { if (responseIndex == i) { response = attributes.remove(i); break; } } AttributeDataset data = new AttributeDataset(relationName, attributes.toArray(new Attribute[attributes.size()]), response); while (true) { // Check if end of file reached. getFirstToken(tokenizer); if (tokenizer.ttype == StreamTokenizer.TT_EOF) { break; } // Parse instance if (tokenizer.ttype == '{') { readSparseInstance(tokenizer, data, attr); } else { readInstance(tokenizer, data, attr); } } for (Attribute attribute : attributes) { if (attribute instanceof NominalAttribute) { NominalAttribute a = (NominalAttribute) attribute; a.setOpen(false); } if (attribute instanceof StringAttribute) { StringAttribute a = (StringAttribute) attribute; a.setOpen(false); } } return data; } }
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Parse a dataset from given stream.
[ "Parse", "a", "dataset", "from", "given", "stream", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L395-L449
17,324
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.readInstance
private void readInstance(StreamTokenizer tokenizer, AttributeDataset data, Attribute[] attributes) throws IOException, ParseException { double[] x = responseIndex >= 0 ? new double[attributes.length - 1] : new double[attributes.length]; double y = Double.NaN; // Get values for all attributes. for (int i = 0, k = 0; i < attributes.length; i++) { // Get next token if (i > 0) { getNextToken(tokenizer); } if (i == responseIndex) { if (tokenizer.ttype == '?') { y = Double.NaN; } else { y = attributes[i].valueOf(tokenizer.sval); } } else { if (tokenizer.ttype == '?') { x[k++] = Double.NaN; } else { x[k++] = attributes[i].valueOf(tokenizer.sval); } } } if (Double.isNaN(y)) data.add(x); else data.add(x, y); }
java
private void readInstance(StreamTokenizer tokenizer, AttributeDataset data, Attribute[] attributes) throws IOException, ParseException { double[] x = responseIndex >= 0 ? new double[attributes.length - 1] : new double[attributes.length]; double y = Double.NaN; // Get values for all attributes. for (int i = 0, k = 0; i < attributes.length; i++) { // Get next token if (i > 0) { getNextToken(tokenizer); } if (i == responseIndex) { if (tokenizer.ttype == '?') { y = Double.NaN; } else { y = attributes[i].valueOf(tokenizer.sval); } } else { if (tokenizer.ttype == '?') { x[k++] = Double.NaN; } else { x[k++] = attributes[i].valueOf(tokenizer.sval); } } } if (Double.isNaN(y)) data.add(x); else data.add(x, y); }
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Reads a single instance. @throws ParseException if the information is not read successfully
[ "Reads", "a", "single", "instance", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L455-L482
17,325
haifengl/smile
data/src/main/java/smile/data/parser/ArffParser.java
ArffParser.readSparseInstance
private void readSparseInstance(StreamTokenizer tokenizer, AttributeDataset data, Attribute[] attributes) throws IOException, ParseException { double[] x = responseIndex >= 0 ? new double[attributes.length - 1] : new double[attributes.length]; double y = Double.NaN; int index = -1; // Get values for all attributes. do { getNextToken(tokenizer); // end of instance if (tokenizer.ttype == '}') { break; } String s = tokenizer.sval.trim(); if (index < 0) { index = Integer.parseInt(s); if (index < 0 || index >= attributes.length) { throw new ParseException("Invalid attribute index: " + index, tokenizer.lineno()); } } else { String val = s; if (index != responseIndex) { if (val.equals("?")) { x[index] = Double.NaN; } else { x[index] = attributes[index].valueOf(val); } } else { if (val.equals("?")) { y = Double.NaN; } else { y = attributes[index].valueOf(val); } } index = -1; } } while (tokenizer.ttype == StreamTokenizer.TT_WORD); if (Double.isNaN(y)) data.add(x); else data.add(x, y); }
java
private void readSparseInstance(StreamTokenizer tokenizer, AttributeDataset data, Attribute[] attributes) throws IOException, ParseException { double[] x = responseIndex >= 0 ? new double[attributes.length - 1] : new double[attributes.length]; double y = Double.NaN; int index = -1; // Get values for all attributes. do { getNextToken(tokenizer); // end of instance if (tokenizer.ttype == '}') { break; } String s = tokenizer.sval.trim(); if (index < 0) { index = Integer.parseInt(s); if (index < 0 || index >= attributes.length) { throw new ParseException("Invalid attribute index: " + index, tokenizer.lineno()); } } else { String val = s; if (index != responseIndex) { if (val.equals("?")) { x[index] = Double.NaN; } else { x[index] = attributes[index].valueOf(val); } } else { if (val.equals("?")) { y = Double.NaN; } else { y = attributes[index].valueOf(val); } } index = -1; } } while (tokenizer.ttype == StreamTokenizer.TT_WORD); if (Double.isNaN(y)) data.add(x); else data.add(x, y); }
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Reads a sparse instance using the tokenizer. @throws ParseException if the information is not read successfully
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/parser/ArffParser.java#L488-L532
17,326
haifengl/smile
math/src/main/java/smile/math/matrix/Lanczos.java
Lanczos.startv
private static double startv(Matrix A, double[][] q, double[][] wptr, int step) { // get initial vector; default is random double rnm = Math.dot(wptr[0], wptr[0]); double[] r = wptr[0]; for (int id = 0; id < 3; id++) { if (id > 0 || step > 0 || rnm == 0) { for (int i = 0; i < r.length; i++) { r[i] = Math.random() - 0.5; } } Math.copy(wptr[0], wptr[3]); // apply operator to put r in range (essential if m singular) A.ax(wptr[3], wptr[0]); Math.copy(wptr[0], wptr[3]); rnm = Math.dot(wptr[0], wptr[3]); if (rnm > 0.0) { break; } } // fatal error if (rnm <= 0.0) { logger.error("Lanczos method was unable to find a starting vector within range."); return -1; } if (step > 0) { for (int i = 0; i < step; i++) { double t = Math.dot(wptr[3], q[i]); Math.axpy(-t, q[i], wptr[0]); } // make sure q[step] is orthogonal to q[step-1] double t = Math.dot(wptr[4], wptr[0]); Math.axpy(-t, wptr[2], wptr[0]); Math.copy(wptr[0], wptr[3]); t = Math.dot(wptr[3], wptr[0]); if (t <= Math.EPSILON * rnm) { t = 0.0; } rnm = t; } return Math.sqrt(rnm); }
java
private static double startv(Matrix A, double[][] q, double[][] wptr, int step) { // get initial vector; default is random double rnm = Math.dot(wptr[0], wptr[0]); double[] r = wptr[0]; for (int id = 0; id < 3; id++) { if (id > 0 || step > 0 || rnm == 0) { for (int i = 0; i < r.length; i++) { r[i] = Math.random() - 0.5; } } Math.copy(wptr[0], wptr[3]); // apply operator to put r in range (essential if m singular) A.ax(wptr[3], wptr[0]); Math.copy(wptr[0], wptr[3]); rnm = Math.dot(wptr[0], wptr[3]); if (rnm > 0.0) { break; } } // fatal error if (rnm <= 0.0) { logger.error("Lanczos method was unable to find a starting vector within range."); return -1; } if (step > 0) { for (int i = 0; i < step; i++) { double t = Math.dot(wptr[3], q[i]); Math.axpy(-t, q[i], wptr[0]); } // make sure q[step] is orthogonal to q[step-1] double t = Math.dot(wptr[4], wptr[0]); Math.axpy(-t, wptr[2], wptr[0]); Math.copy(wptr[0], wptr[3]); t = Math.dot(wptr[3], wptr[0]); if (t <= Math.EPSILON * rnm) { t = 0.0; } rnm = t; } return Math.sqrt(rnm); }
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Generate a starting vector in r and returns |r|. It returns zero if the range is spanned, and throws exception if no starting vector within range of operator can be found. @param step starting index for a Lanczos run
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/Lanczos.java#L322-L367
17,327
haifengl/smile
math/src/main/java/smile/math/matrix/Lanczos.java
Lanczos.ortbnd
private static void ortbnd(double[] alf, double[] bet, double[] eta, double[] oldeta, int step, double rnm, double eps) { if (step < 1) { return; } if (0 != rnm) { if (step > 1) { oldeta[0] = (bet[1] * eta[1] + (alf[0] - alf[step]) * eta[0] - bet[step] * oldeta[0]) / rnm + eps; } for (int i = 1; i <= step - 2; i++) { oldeta[i] = (bet[i + 1] * eta[i + 1] + (alf[i] - alf[step]) * eta[i] + bet[i] * eta[i - 1] - bet[step] * oldeta[i]) / rnm + eps; } } oldeta[step - 1] = eps; for (int i = 0; i < step; i++) { double swap = eta[i]; eta[i] = oldeta[i]; oldeta[i] = swap; } eta[step] = eps; }
java
private static void ortbnd(double[] alf, double[] bet, double[] eta, double[] oldeta, int step, double rnm, double eps) { if (step < 1) { return; } if (0 != rnm) { if (step > 1) { oldeta[0] = (bet[1] * eta[1] + (alf[0] - alf[step]) * eta[0] - bet[step] * oldeta[0]) / rnm + eps; } for (int i = 1; i <= step - 2; i++) { oldeta[i] = (bet[i + 1] * eta[i + 1] + (alf[i] - alf[step]) * eta[i] + bet[i] * eta[i - 1] - bet[step] * oldeta[i]) / rnm + eps; } } oldeta[step - 1] = eps; for (int i = 0; i < step; i++) { double swap = eta[i]; eta[i] = oldeta[i]; oldeta[i] = swap; } eta[step] = eps; }
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Update the eta recurrence. @param alf array to store diagonal of the tridiagonal matrix T @param bet array to store off-diagonal of T @param eta on input, orthogonality estimate of Lanczos vectors at step j. On output, orthogonality estimate of Lanczos vectors at step j+1 . @param oldeta on input, orthogonality estimate of Lanczos vectors at step j-1 On output orthogonality estimate of Lanczos vectors at step j @param step dimension of T @param rnm norm of the next residual vector @param eps roundoff estimate for dot product of two unit vectors
[ "Update", "the", "eta", "recurrence", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/Lanczos.java#L381-L404
17,328
haifengl/smile
math/src/main/java/smile/math/matrix/Lanczos.java
Lanczos.purge
private static double purge(int ll, double[][] Q, double[] r, double[] q, double[] ra, double[] qa, double[] eta, double[] oldeta, int step, double rnm, double tol, double eps, double reps) { if (step < ll + 2) { return rnm; } double t, tq, tr; int k = idamax(step - (ll + 1), eta, ll, 1) + ll; if (Math.abs(eta[k]) > reps) { double reps1 = eps / reps; int iteration = 0; boolean flag = true; while (iteration < 2 && flag) { if (rnm > tol) { // bring in a lanczos vector t and orthogonalize both // r and q against it tq = 0.0; tr = 0.0; for (int i = ll; i < step; i++) { t = -Math.dot(qa, Q[i]); tq += Math.abs(t); Math.axpy(t, Q[i], q); t = -Math.dot(ra, Q[i]); tr += Math.abs(t); Math.axpy(t, Q[i], r); } Math.copy(q, qa); t = -Math.dot(r, qa); tr += Math.abs(t); Math.axpy(t, q, r); Math.copy(r, ra); rnm = Math.sqrt(Math.dot(ra, r)); if (tq <= reps1 && tr <= reps1 * rnm) { flag = false; } } iteration++; } for (int i = ll; i <= step; i++) { eta[i] = eps; oldeta[i] = eps; } } return rnm; }
java
private static double purge(int ll, double[][] Q, double[] r, double[] q, double[] ra, double[] qa, double[] eta, double[] oldeta, int step, double rnm, double tol, double eps, double reps) { if (step < ll + 2) { return rnm; } double t, tq, tr; int k = idamax(step - (ll + 1), eta, ll, 1) + ll; if (Math.abs(eta[k]) > reps) { double reps1 = eps / reps; int iteration = 0; boolean flag = true; while (iteration < 2 && flag) { if (rnm > tol) { // bring in a lanczos vector t and orthogonalize both // r and q against it tq = 0.0; tr = 0.0; for (int i = ll; i < step; i++) { t = -Math.dot(qa, Q[i]); tq += Math.abs(t); Math.axpy(t, Q[i], q); t = -Math.dot(ra, Q[i]); tr += Math.abs(t); Math.axpy(t, Q[i], r); } Math.copy(q, qa); t = -Math.dot(r, qa); tr += Math.abs(t); Math.axpy(t, q, r); Math.copy(r, ra); rnm = Math.sqrt(Math.dot(ra, r)); if (tq <= reps1 && tr <= reps1 * rnm) { flag = false; } } iteration++; } for (int i = ll; i <= step; i++) { eta[i] = eps; oldeta[i] = eps; } } return rnm; }
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Examine the state of orthogonality between the new Lanczos vector and the previous ones to decide whether re-orthogonalization should be performed. @param ll number of intitial Lanczos vectors in local orthog. @param r on input, residual vector to become next Lanczos vector. On output, residual vector orthogonalized against previous Lanczos. @param q on input, current Lanczos vector. On Output, current Lanczos vector orthogonalized against previous ones. @param ra previous Lanczos vector @param qa previous Lanczos vector @param eta state of orthogonality between r and prev. Lanczos vectors @param oldeta state of orthogonality between q and prev. Lanczos vectors
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/Lanczos.java#L420-L465
17,329
haifengl/smile
math/src/main/java/smile/math/matrix/Lanczos.java
Lanczos.idamax
private static int idamax(int n, double[] dx, int ix0, int incx) { int ix, imax; double dmax; if (n < 1) { return -1; } if (n == 1) { return 0; } if (incx == 0) { return -1; } ix = (incx < 0) ? ix0 + ((-n + 1) * incx) : ix0; imax = ix; dmax = Math.abs(dx[ix]); for (int i = 1; i < n; i++) { ix += incx; double dtemp = Math.abs(dx[ix]); if (dtemp > dmax) { dmax = dtemp; imax = ix; } } return imax; }
java
private static int idamax(int n, double[] dx, int ix0, int incx) { int ix, imax; double dmax; if (n < 1) { return -1; } if (n == 1) { return 0; } if (incx == 0) { return -1; } ix = (incx < 0) ? ix0 + ((-n + 1) * incx) : ix0; imax = ix; dmax = Math.abs(dx[ix]); for (int i = 1; i < n; i++) { ix += incx; double dtemp = Math.abs(dx[ix]); if (dtemp > dmax) { dmax = dtemp; imax = ix; } } return imax; }
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Find the index of element having maximum absolute value.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/Lanczos.java#L470-L494
17,330
haifengl/smile
math/src/main/java/smile/math/matrix/Lanczos.java
Lanczos.store
private static void store(double[][] q, int j, double[] s) { if (null == q[j]) { q[j] = s.clone(); } else { Math.copy(s, q[j]); } }
java
private static void store(double[][] q, int j, double[] s) { if (null == q[j]) { q[j] = s.clone(); } else { Math.copy(s, q[j]); } }
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Based on the input operation flag, stores to or retrieves from memory a vector. @param s contains the vector to be stored
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/Lanczos.java#L565-L571
17,331
haifengl/smile
math/src/main/java/smile/math/distance/ChebyshevDistance.java
ChebyshevDistance.d
public static double d(int[] x, int[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); double dist = 0.0; for (int i = 0; i < x.length; i++) { double d = Math.abs(x[i] - y[i]); if (dist < d) dist = d; } return dist; }
java
public static double d(int[] x, int[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); double dist = 0.0; for (int i = 0; i < x.length; i++) { double d = Math.abs(x[i] - y[i]); if (dist < d) dist = d; } return dist; }
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Chebyshev distance between the two arrays of type integer.
[ "Chebyshev", "distance", "between", "the", "two", "arrays", "of", "type", "integer", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/distance/ChebyshevDistance.java#L43-L55
17,332
haifengl/smile
demo/src/main/java/smile/demo/SmileDemo.java
SmileDemo.valueChanged
@Override public void valueChanged(TreeSelectionEvent e) { DefaultMutableTreeNode node = (DefaultMutableTreeNode) tree.getLastSelectedPathComponent(); if (node != null && node.isLeaf()) { int pos = workspace.getDividerLocation(); workspace.setTopComponent((JPanel) node.getUserObject()); workspace.setDividerLocation(pos); } }
java
@Override public void valueChanged(TreeSelectionEvent e) { DefaultMutableTreeNode node = (DefaultMutableTreeNode) tree.getLastSelectedPathComponent(); if (node != null && node.isLeaf()) { int pos = workspace.getDividerLocation(); workspace.setTopComponent((JPanel) node.getUserObject()); workspace.setDividerLocation(pos); } }
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Required by TreeSelectionListener interface.
[ "Required", "by", "TreeSelectionListener", "interface", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/demo/src/main/java/smile/demo/SmileDemo.java#L183-L192
17,333
haifengl/smile
core/src/main/java/smile/feature/FeatureTransform.java
FeatureTransform.learn
public void learn(double[][] data) { int p = data[0].length; Attribute[] attributes = new Attribute[p]; for (int i = 0; i < p; i++) { attributes[i] = new NumericAttribute("V"+i); } learn(attributes, data); }
java
public void learn(double[][] data) { int p = data[0].length; Attribute[] attributes = new Attribute[p]; for (int i = 0; i < p; i++) { attributes[i] = new NumericAttribute("V"+i); } learn(attributes, data); }
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Learns transformation parameters from a dataset. All features are assumed numeric. @param data The training data.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/feature/FeatureTransform.java#L55-L62
17,334
haifengl/smile
core/src/main/java/smile/feature/FeatureTransform.java
FeatureTransform.transform
public double[][] transform(double[][] x) { double[][] y = new double[x.length][]; for (int i = 0; i < y.length; i++) { y[i] = transform(x[i]); } return y; }
java
public double[][] transform(double[][] x) { double[][] y = new double[x.length][]; for (int i = 0; i < y.length; i++) { y[i] = transform(x[i]); } return y; }
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Transform an array of feature vectors. @param x an array of feature vectors. The feature vectors may be modified on output if copy is false. @return the transformed feature vectors.
[ "Transform", "an", "array", "of", "feature", "vectors", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/feature/FeatureTransform.java#L86-L93
17,335
haifengl/smile
math/src/main/java/smile/math/distance/CorrelationDistance.java
CorrelationDistance.d
@Override public double d(double[] x, double[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); return 1 - Math.cor(x, y); }
java
@Override public double d(double[] x, double[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); return 1 - Math.cor(x, y); }
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Pearson correlation distance between the two arrays of type double.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/distance/CorrelationDistance.java#L44-L50
17,336
haifengl/smile
math/src/main/java/smile/math/distance/CorrelationDistance.java
CorrelationDistance.pearson
public static double pearson(int[] x, int[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); return 1 - Math.cor(x, y); }
java
public static double pearson(int[] x, int[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); return 1 - Math.cor(x, y); }
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Pearson correlation distance between the two arrays of type int.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/distance/CorrelationDistance.java#L55-L60
17,337
haifengl/smile
math/src/main/java/smile/math/distance/CorrelationDistance.java
CorrelationDistance.spearman
public static double spearman(int[] x, int[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); return 1 - Math.spearman(x, y); }
java
public static double spearman(int[] x, int[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); return 1 - Math.spearman(x, y); }
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Spearman correlation distance between the two arrays of type int.
[ "Spearman", "correlation", "distance", "between", "the", "two", "arrays", "of", "type", "int", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/distance/CorrelationDistance.java#L85-L90
17,338
haifengl/smile
math/src/main/java/smile/math/distance/CorrelationDistance.java
CorrelationDistance.kendall
public static double kendall(int[] x, int[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); return 1 - Math.kendall(x, y); }
java
public static double kendall(int[] x, int[] y) { if (x.length != y.length) throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length)); return 1 - Math.kendall(x, y); }
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Kendall rank correlation distance between the two arrays of type int.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/distance/CorrelationDistance.java#L115-L120
17,339
haifengl/smile
math/src/main/java/smile/sort/SortUtils.java
SortUtils.swap
public static void swap(float arr[], int i, int j) { float a = arr[i]; arr[i] = arr[j]; arr[j] = a; }
java
public static void swap(float arr[], int i, int j) { float a = arr[i]; arr[i] = arr[j]; arr[j] = a; }
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Swap two positions.
[ "Swap", "two", "positions", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/sort/SortUtils.java#L43-L47
17,340
haifengl/smile
core/src/main/java/smile/clustering/BBDTree.java
BBDTree.getNodeCost
private double getNodeCost(Node node, double[] center) { int d = center.length; double scatter = 0.0; for (int i = 0; i < d; i++) { double x = (node.sum[i] / node.count) - center[i]; scatter += x * x; } return node.cost + node.count * scatter; }
java
private double getNodeCost(Node node, double[] center) { int d = center.length; double scatter = 0.0; for (int i = 0; i < d; i++) { double x = (node.sum[i] / node.count) - center[i]; scatter += x * x; } return node.cost + node.count * scatter; }
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Returns the total contribution of all data in the given kd-tree node, assuming they are all assigned to a mean at the given location. sum_{x \in node} ||x - mean||^2. If c denotes the mean of mass of the data in this node and n denotes the number of data in it, then this quantity is given by n * ||c - mean||^2 + sum_{x \in node} ||x - c||^2 The sum is precomputed for each node as cost. This formula follows from expanding both sides as dot products.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/clustering/BBDTree.java#L241-L249
17,341
haifengl/smile
core/src/main/java/smile/clustering/BBDTree.java
BBDTree.clustering
public double clustering(double[][] centroids, double[][] sums, int[] counts, int[] membership) { int k = centroids.length; Arrays.fill(counts, 0); int[] candidates = new int[k]; for (int i = 0; i < k; i++) { candidates[i] = i; Arrays.fill(sums[i], 0.0); } return filter(root, centroids, candidates, k, sums, counts, membership); }
java
public double clustering(double[][] centroids, double[][] sums, int[] counts, int[] membership) { int k = centroids.length; Arrays.fill(counts, 0); int[] candidates = new int[k]; for (int i = 0; i < k; i++) { candidates[i] = i; Arrays.fill(sums[i], 0.0); } return filter(root, centroids, candidates, k, sums, counts, membership); }
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Given k cluster centroids, this method assigns data to nearest centroids. The return value is the distortion to the centroids. The parameter sums will hold the sum of data for each cluster. The parameter counts hold the number of data of each cluster. If membership is not null, it should be an array of size n that will be filled with the index of the cluster [0 - k) that each data point is assigned to.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/clustering/BBDTree.java#L259-L270
17,342
haifengl/smile
core/src/main/java/smile/imputation/KMeansImputation.java
KMeansImputation.columnAverageImpute
static void columnAverageImpute(double[][] data) throws MissingValueImputationException { for (int j = 0; j < data[0].length; j++) { int n = 0; double sum = 0.0; for (int i = 0; i < data.length; i++) { if (!Double.isNaN(data[i][j])) { n++; sum += data[i][j]; } } if (n == 0) { continue; } if (n < data.length) { double avg = sum / n; for (int i = 0; i < data.length; i++) { if (Double.isNaN(data[i][j])) { data[i][j] = avg; } } } } }
java
static void columnAverageImpute(double[][] data) throws MissingValueImputationException { for (int j = 0; j < data[0].length; j++) { int n = 0; double sum = 0.0; for (int i = 0; i < data.length; i++) { if (!Double.isNaN(data[i][j])) { n++; sum += data[i][j]; } } if (n == 0) { continue; } if (n < data.length) { double avg = sum / n; for (int i = 0; i < data.length; i++) { if (Double.isNaN(data[i][j])) { data[i][j] = avg; } } } } }
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Impute the missing values with column averages. @param data data with missing values. @throws smile.imputation.MissingValueImputationException
[ "Impute", "the", "missing", "values", "with", "column", "averages", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/imputation/KMeansImputation.java#L111-L136
17,343
haifengl/smile
core/src/main/java/smile/validation/Validation.java
Validation.loocv
public static <T> double loocv(ClassifierTrainer<T> trainer, T[] x, int[] y) { int m = 0; int n = x.length; LOOCV loocv = new LOOCV(n); for (int i = 0; i < n; i++) { T[] trainx = Math.slice(x, loocv.train[i]); int[] trainy = Math.slice(y, loocv.train[i]); Classifier<T> classifier = trainer.train(trainx, trainy); if (classifier.predict(x[loocv.test[i]]) == y[loocv.test[i]]) { m++; } } return (double) m / n; }
java
public static <T> double loocv(ClassifierTrainer<T> trainer, T[] x, int[] y) { int m = 0; int n = x.length; LOOCV loocv = new LOOCV(n); for (int i = 0; i < n; i++) { T[] trainx = Math.slice(x, loocv.train[i]); int[] trainy = Math.slice(y, loocv.train[i]); Classifier<T> classifier = trainer.train(trainx, trainy); if (classifier.predict(x[loocv.test[i]]) == y[loocv.test[i]]) { m++; } } return (double) m / n; }
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Leave-one-out cross validation of a classification model. @param <T> the data type of input objects. @param trainer a classifier trainer that is properly parameterized. @param x the test data set. @param y the test data labels. @return the accuracy on test dataset
[ "Leave", "-", "one", "-", "out", "cross", "validation", "of", "a", "classification", "model", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/validation/Validation.java#L169-L186
17,344
haifengl/smile
core/src/main/java/smile/validation/Validation.java
Validation.cv
public static <T> double cv(int k, ClassifierTrainer<T> trainer, T[] x, int[] y, ClassificationMeasure measure) { if (k < 2) { throw new IllegalArgumentException("Invalid k for k-fold cross validation: " + k); } int n = x.length; int[] predictions = new int[n]; CrossValidation cv = new CrossValidation(n, k); for (int i = 0; i < k; i++) { T[] trainx = Math.slice(x, cv.train[i]); int[] trainy = Math.slice(y, cv.train[i]); Classifier<T> classifier = trainer.train(trainx, trainy); for (int j : cv.test[i]) { predictions[j] = classifier.predict(x[j]); } } return measure.measure(y, predictions); }
java
public static <T> double cv(int k, ClassifierTrainer<T> trainer, T[] x, int[] y, ClassificationMeasure measure) { if (k < 2) { throw new IllegalArgumentException("Invalid k for k-fold cross validation: " + k); } int n = x.length; int[] predictions = new int[n]; CrossValidation cv = new CrossValidation(n, k); for (int i = 0; i < k; i++) { T[] trainx = Math.slice(x, cv.train[i]); int[] trainy = Math.slice(y, cv.train[i]); Classifier<T> classifier = trainer.train(trainx, trainy); for (int j : cv.test[i]) { predictions[j] = classifier.predict(x[j]); } } return measure.measure(y, predictions); }
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Cross validation of a classification model. @param <T> the data type of input objects. @param k k-fold cross validation. @param trainer a classifier trainer that is properly parameterized. @param x the test data set. @param y the test data labels. @param measure the performance measure of classification. @return the test results with the same size of order of measures
[ "Cross", "validation", "of", "a", "classification", "model", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/validation/Validation.java#L410-L431
17,345
haifengl/smile
core/src/main/java/smile/validation/Validation.java
Validation.cv
public static <T> double cv(int k, RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure measure) { if (k < 2) { throw new IllegalArgumentException("Invalid k for k-fold cross validation: " + k); } int n = x.length; double[] predictions = new double[n]; CrossValidation cv = new CrossValidation(n, k); for (int i = 0; i < k; i++) { T[] trainx = Math.slice(x, cv.train[i]); double[] trainy = Math.slice(y, cv.train[i]); Regression<T> model = trainer.train(trainx, trainy); for (int j : cv.test[i]) { predictions[j] = model.predict(x[j]); } } return measure.measure(y, predictions); }
java
public static <T> double cv(int k, RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure measure) { if (k < 2) { throw new IllegalArgumentException("Invalid k for k-fold cross validation: " + k); } int n = x.length; double[] predictions = new double[n]; CrossValidation cv = new CrossValidation(n, k); for (int i = 0; i < k; i++) { T[] trainx = Math.slice(x, cv.train[i]); double[] trainy = Math.slice(y, cv.train[i]); Regression<T> model = trainer.train(trainx, trainy); for (int j : cv.test[i]) { predictions[j] = model.predict(x[j]); } } return measure.measure(y, predictions); }
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Cross validation of a regression model. @param <T> the data type of input objects. @param k k-fold cross validation. @param trainer a regression model trainer that is properly parameterized. @param x the test data set. @param y the test data response values. @param measure the performance measure of regression. @return the test results with the same size of order of measures
[ "Cross", "validation", "of", "a", "regression", "model", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/validation/Validation.java#L484-L505
17,346
haifengl/smile
core/src/main/java/smile/validation/Validation.java
Validation.bootstrap
public static <T> double[] bootstrap(int k, ClassifierTrainer<T> trainer, T[] x, int[] y) { if (k < 2) { throw new IllegalArgumentException("Invalid k for k-fold bootstrap: " + k); } int n = x.length; double[] results = new double[k]; Accuracy measure = new Accuracy(); Bootstrap bootstrap = new Bootstrap(n, k); for (int i = 0; i < k; i++) { T[] trainx = Math.slice(x, bootstrap.train[i]); int[] trainy = Math.slice(y, bootstrap.train[i]); Classifier<T> classifier = trainer.train(trainx, trainy); int nt = bootstrap.test[i].length; int[] truth = new int[nt]; int[] predictions = new int[nt]; for (int j = 0; j < nt; j++) { int l = bootstrap.test[i][j]; truth[j] = y[l]; predictions[j] = classifier.predict(x[l]); } results[i] = measure.measure(truth, predictions); } return results; }
java
public static <T> double[] bootstrap(int k, ClassifierTrainer<T> trainer, T[] x, int[] y) { if (k < 2) { throw new IllegalArgumentException("Invalid k for k-fold bootstrap: " + k); } int n = x.length; double[] results = new double[k]; Accuracy measure = new Accuracy(); Bootstrap bootstrap = new Bootstrap(n, k); for (int i = 0; i < k; i++) { T[] trainx = Math.slice(x, bootstrap.train[i]); int[] trainy = Math.slice(y, bootstrap.train[i]); Classifier<T> classifier = trainer.train(trainx, trainy); int nt = bootstrap.test[i].length; int[] truth = new int[nt]; int[] predictions = new int[nt]; for (int j = 0; j < nt; j++) { int l = bootstrap.test[i][j]; truth[j] = y[l]; predictions[j] = classifier.predict(x[l]); } results[i] = measure.measure(truth, predictions); } return results; }
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Bootstrap accuracy estimation of a classification model. @param <T> the data type of input objects. @param k k-round bootstrap estimation. @param trainer a classifier trainer that is properly parameterized. @param x the test data set. @param y the test data labels. @return the k-round accuracies
[ "Bootstrap", "accuracy", "estimation", "of", "a", "classification", "model", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/validation/Validation.java#L557-L586
17,347
haifengl/smile
core/src/main/java/smile/validation/Validation.java
Validation.bootstrap
public static <T> double[] bootstrap(int k, RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure measure) { if (k < 2) { throw new IllegalArgumentException("Invalid k for k-fold bootstrap: " + k); } int n = x.length; double[] results = new double[k]; Bootstrap bootstrap = new Bootstrap(n, k); for (int i = 0; i < k; i++) { T[] trainx = Math.slice(x, bootstrap.train[i]); double[] trainy = Math.slice(y, bootstrap.train[i]); Regression<T> model = trainer.train(trainx, trainy); int nt = bootstrap.test[i].length; double[] truth = new double[nt]; double[] predictions = new double[nt]; for (int j = 0; j < nt; j++) { int l = bootstrap.test[i][j]; truth[j] = y[l]; predictions[j] = model.predict(x[l]); } results[i] = measure.measure(truth, predictions); } return results; }
java
public static <T> double[] bootstrap(int k, RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure measure) { if (k < 2) { throw new IllegalArgumentException("Invalid k for k-fold bootstrap: " + k); } int n = x.length; double[] results = new double[k]; Bootstrap bootstrap = new Bootstrap(n, k); for (int i = 0; i < k; i++) { T[] trainx = Math.slice(x, bootstrap.train[i]); double[] trainy = Math.slice(y, bootstrap.train[i]); Regression<T> model = trainer.train(trainx, trainy); int nt = bootstrap.test[i].length; double[] truth = new double[nt]; double[] predictions = new double[nt]; for (int j = 0; j < nt; j++) { int l = bootstrap.test[i][j]; truth[j] = y[l]; predictions[j] = model.predict(x[l]); } results[i] = measure.measure(truth, predictions); } return results; }
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Bootstrap performance estimation of a regression model. @param <T> the data type of input objects. @param k k-fold bootstrap estimation. @param trainer a regression model trainer that is properly parameterized. @param x the test data set. @param y the test data response values. @param measure the performance measure of regression. @return k-by-m test result matrix, where k is the number of bootstrap samples and m is the number of performance measures.
[ "Bootstrap", "performance", "estimation", "of", "a", "regression", "model", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/validation/Validation.java#L728-L756
17,348
haifengl/smile
data/src/main/java/smile/data/BinarySparseDataset.java
BinarySparseDataset.toSparseMatrix
public SparseMatrix toSparseMatrix() { int[] pos = new int[numColumns]; int[] colIndex = new int[numColumns + 1]; for (int i = 0; i < numColumns; i++) { colIndex[i + 1] = colIndex[i] + colSize[i]; } int nrows = size(); int[] rowIndex = new int[n]; double[] x = new double[n]; for (int i = 0; i < nrows; i++) { for (int j : get(i).x) { int k = colIndex[j] + pos[j]; rowIndex[k] = i; x[k] = 1; pos[j]++; } } return new SparseMatrix(nrows, numColumns, x, rowIndex, colIndex); }
java
public SparseMatrix toSparseMatrix() { int[] pos = new int[numColumns]; int[] colIndex = new int[numColumns + 1]; for (int i = 0; i < numColumns; i++) { colIndex[i + 1] = colIndex[i] + colSize[i]; } int nrows = size(); int[] rowIndex = new int[n]; double[] x = new double[n]; for (int i = 0; i < nrows; i++) { for (int j : get(i).x) { int k = colIndex[j] + pos[j]; rowIndex[k] = i; x[k] = 1; pos[j]++; } } return new SparseMatrix(nrows, numColumns, x, rowIndex, colIndex); }
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Convert into Harwell-Boeing column-compressed sparse matrix format.
[ "Convert", "into", "Harwell", "-", "Boeing", "column", "-", "compressed", "sparse", "matrix", "format", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/BinarySparseDataset.java#L165-L187
17,349
haifengl/smile
nlp/src/main/java/smile/nlp/SimpleCorpus.java
SimpleCorpus.add
public Text add(String id, String title, String body) { ArrayList<String> bag = new ArrayList<>(); for (String sentence : splitter.split(body)) { String[] tokens = tokenizer.split(sentence); for (int i = 0; i < tokens.length; i++) { tokens[i] = tokens[i].toLowerCase(); } for (String w : tokens) { boolean keep = true; if (punctuations != null && punctuations.contains(w)) { keep = false; } else if (stopWords != null && stopWords.contains(w)) { keep = false; } if (keep) { size++; bag.add(w); Integer f = freq.get(w); if (f == null) { f = 1; } else { f = f + 1; } freq.put(w, f); } } for (int i = 0; i < tokens.length - 1; i++) { String w1 = tokens[i]; String w2 = tokens[i + 1]; if (freq.containsKey(w1) && freq.containsKey(w2)) { Bigram bigram = new Bigram(w1, w2); Integer f = freq2.get(bigram); if (f == null) { f = 1; } else { f = f + 1; } freq2.put(bigram, f); } } } String[] words = new String[bag.size()]; for (int i = 0; i < words.length; i++) { words[i] = bag.get(i); } SimpleText doc = new SimpleText(id, title, body, words); docs.add(doc); for (String term : doc.unique()) { List<SimpleText> hit = invertedFile.get(term); if (hit == null) { hit = new ArrayList<>(); invertedFile.put(term, hit); } hit.add(doc); } return doc; }
java
public Text add(String id, String title, String body) { ArrayList<String> bag = new ArrayList<>(); for (String sentence : splitter.split(body)) { String[] tokens = tokenizer.split(sentence); for (int i = 0; i < tokens.length; i++) { tokens[i] = tokens[i].toLowerCase(); } for (String w : tokens) { boolean keep = true; if (punctuations != null && punctuations.contains(w)) { keep = false; } else if (stopWords != null && stopWords.contains(w)) { keep = false; } if (keep) { size++; bag.add(w); Integer f = freq.get(w); if (f == null) { f = 1; } else { f = f + 1; } freq.put(w, f); } } for (int i = 0; i < tokens.length - 1; i++) { String w1 = tokens[i]; String w2 = tokens[i + 1]; if (freq.containsKey(w1) && freq.containsKey(w2)) { Bigram bigram = new Bigram(w1, w2); Integer f = freq2.get(bigram); if (f == null) { f = 1; } else { f = f + 1; } freq2.put(bigram, f); } } } String[] words = new String[bag.size()]; for (int i = 0; i < words.length; i++) { words[i] = bag.get(i); } SimpleText doc = new SimpleText(id, title, body, words); docs.add(doc); for (String term : doc.unique()) { List<SimpleText> hit = invertedFile.get(term); if (hit == null) { hit = new ArrayList<>(); invertedFile.put(term, hit); } hit.add(doc); } return doc; }
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Add a document to the corpus.
[ "Add", "a", "document", "to", "the", "corpus", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/nlp/src/main/java/smile/nlp/SimpleCorpus.java#L108-L176
17,350
haifengl/smile
graph/src/main/java/smile/graph/AdjacencyMatrix.java
AdjacencyMatrix.dijkstra
public double[] dijkstra(int s, boolean weighted) { double[] wt = new double[n]; Arrays.fill(wt, Double.POSITIVE_INFINITY); PriorityQueue queue = new PriorityQueue(wt); for (int v = 0; v < n; v++) { queue.insert(v); } wt[s] = 0.0; queue.lower(s); while (!queue.empty()) { int v = queue.poll(); if (!Double.isInfinite(wt[v])) { for (int w = 0; w < n; w++) { if (graph[v][w] != 0.0) { double p = weighted ? wt[v] + graph[v][w] : wt[v] + 1; if (p < wt[w]) { wt[w] = p; queue.lower(w); } } } } } return wt; }
java
public double[] dijkstra(int s, boolean weighted) { double[] wt = new double[n]; Arrays.fill(wt, Double.POSITIVE_INFINITY); PriorityQueue queue = new PriorityQueue(wt); for (int v = 0; v < n; v++) { queue.insert(v); } wt[s] = 0.0; queue.lower(s); while (!queue.empty()) { int v = queue.poll(); if (!Double.isInfinite(wt[v])) { for (int w = 0; w < n; w++) { if (graph[v][w] != 0.0) { double p = weighted ? wt[v] + graph[v][w] : wt[v] + 1; if (p < wt[w]) { wt[w] = p; queue.lower(w); } } } } } return wt; }
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Calculates the shortest path by Dijkstra algorithm. @param s The source vertex. @param weighted True to calculate weighted path. Otherwise, the edge weights will be ignored. @return The distance to all vertices from the source.
[ "Calculates", "the", "shortest", "path", "by", "Dijkstra", "algorithm", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/graph/src/main/java/smile/graph/AdjacencyMatrix.java#L508-L536
17,351
haifengl/smile
demo/src/main/java/smile/demo/data/classification/ToyData.java
ToyData.sample
public double[][] sample(int n) { double[][] samples = new double[2 * n][]; MultivariateGaussianDistribution[] gauss = new MultivariateGaussianDistribution[k]; for (int i = 0; i < k; i++) { gauss[i] = new MultivariateGaussianDistribution(m[i], v); } for (int i = 0; i < n; i++) { samples[i] = gauss[Math.random(prob)].rand(); } for (int i = 0; i < k; i++) { gauss[i] = new MultivariateGaussianDistribution(m[k + i], v); } for (int i = 0; i < n; i++) { samples[n + i] = gauss[Math.random(prob)].rand(); } return samples; }
java
public double[][] sample(int n) { double[][] samples = new double[2 * n][]; MultivariateGaussianDistribution[] gauss = new MultivariateGaussianDistribution[k]; for (int i = 0; i < k; i++) { gauss[i] = new MultivariateGaussianDistribution(m[i], v); } for (int i = 0; i < n; i++) { samples[i] = gauss[Math.random(prob)].rand(); } for (int i = 0; i < k; i++) { gauss[i] = new MultivariateGaussianDistribution(m[k + i], v); } for (int i = 0; i < n; i++) { samples[n + i] = gauss[Math.random(prob)].rand(); } return samples; }
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Generate n samples from each class.
[ "Generate", "n", "samples", "from", "each", "class", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/demo/src/main/java/smile/demo/data/classification/ToyData.java#L66-L87
17,352
haifengl/smile
core/src/main/java/smile/vq/NeuralMap.java
NeuralMap.purge
public int purge(int minPts) { List<Neuron> outliers = new ArrayList<>(); for (Neuron neuron : neurons) { if (neuron.n < minPts) { outliers.add(neuron); } } neurons.removeAll(outliers); for (Neuron neuron : neurons) { neuron.neighbors.removeAll(outliers); } outliers.clear(); for (Neuron neuron : neurons) { if (neuron.neighbors.isEmpty()) { outliers.add(neuron); } } neurons.removeAll(outliers); return neurons.size(); }
java
public int purge(int minPts) { List<Neuron> outliers = new ArrayList<>(); for (Neuron neuron : neurons) { if (neuron.n < minPts) { outliers.add(neuron); } } neurons.removeAll(outliers); for (Neuron neuron : neurons) { neuron.neighbors.removeAll(outliers); } outliers.clear(); for (Neuron neuron : neurons) { if (neuron.neighbors.isEmpty()) { outliers.add(neuron); } } neurons.removeAll(outliers); return neurons.size(); }
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Removes neurons with the number of samples less than a given threshold. The neurons without neighbors will also be removed. @param minPts neurons will be removed if the number of its points is less than minPts. @return the number of neurons after purging.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/vq/NeuralMap.java#L532-L554
17,353
haifengl/smile
core/src/main/java/smile/clustering/XMeans.java
XMeans.bic
private double bic(int n, int d, double distortion) { double variance = distortion / (n - 1); double p1 = -n * LOG2PI; double p2 = -n * d * Math.log(variance); double p3 = -(n - 1); double L = (p1 + p2 + p3) / 2; int numParameters = d + 1; return L - 0.5 * numParameters * Math.log(n); }
java
private double bic(int n, int d, double distortion) { double variance = distortion / (n - 1); double p1 = -n * LOG2PI; double p2 = -n * d * Math.log(variance); double p3 = -(n - 1); double L = (p1 + p2 + p3) / 2; int numParameters = d + 1; return L - 0.5 * numParameters * Math.log(n); }
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Calculates the BIC for single cluster. @param n the total number of samples. @param d the dimensionality of data. @param distortion the distortion of clusters. @return the BIC score.
[ "Calculates", "the", "BIC", "for", "single", "cluster", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/clustering/XMeans.java#L182-L192
17,354
haifengl/smile
core/src/main/java/smile/clustering/XMeans.java
XMeans.bic
private double bic(int k, int n, int d, double distortion, int[] clusterSize) { double variance = distortion / (n - k); double L = 0.0; for (int i = 0; i < k; i++) { L += logLikelihood(k, n, clusterSize[i], d, variance); } int numParameters = k + k * d; return L - 0.5 * numParameters * Math.log(n); }
java
private double bic(int k, int n, int d, double distortion, int[] clusterSize) { double variance = distortion / (n - k); double L = 0.0; for (int i = 0; i < k; i++) { L += logLikelihood(k, n, clusterSize[i], d, variance); } int numParameters = k + k * d; return L - 0.5 * numParameters * Math.log(n); }
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Calculates the BIC for the given set of centers. @param k the number of clusters. @param n the total number of samples. @param d the dimensionality of data. @param distortion the distortion of clusters. @param clusterSize the number of samples in each cluster. @return the BIC score.
[ "Calculates", "the", "BIC", "for", "the", "given", "set", "of", "centers", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/clustering/XMeans.java#L203-L213
17,355
haifengl/smile
core/src/main/java/smile/clustering/XMeans.java
XMeans.logLikelihood
private static double logLikelihood(int k, int n, int ni, int d, double variance) { double p1 = -ni * LOG2PI; double p2 = -ni * d * Math.log(variance); double p3 = -(ni - k); double p4 = ni * Math.log(ni); double p5 = -ni * Math.log(n); double loglike = (p1 + p2 + p3) / 2 + p4 + p5; return loglike; }
java
private static double logLikelihood(int k, int n, int ni, int d, double variance) { double p1 = -ni * LOG2PI; double p2 = -ni * d * Math.log(variance); double p3 = -(ni - k); double p4 = ni * Math.log(ni); double p5 = -ni * Math.log(n); double loglike = (p1 + p2 + p3) / 2 + p4 + p5; return loglike; }
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Estimate the log-likelihood of the data for the given model. @param k the number of clusters. @param n the total number of samples. @param ni the number of samples belong to this cluster. @param d the dimensionality of data. @param variance the estimated variance of clusters. @return the likelihood estimate
[ "Estimate", "the", "log", "-", "likelihood", "of", "the", "data", "for", "the", "given", "model", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/clustering/XMeans.java#L225-L233
17,356
haifengl/smile
math/src/main/java/smile/math/Random.java
Random.nextDoubles
public void nextDoubles(double[] d, double lo, double hi) { real.nextDoubles(d); double l = hi - lo; int n = d.length; for (int i = 0; i < n; i++) { d[i] = lo + l * d[i]; } }
java
public void nextDoubles(double[] d, double lo, double hi) { real.nextDoubles(d); double l = hi - lo; int n = d.length; for (int i = 0; i < n; i++) { d[i] = lo + l * d[i]; } }
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Generate n uniform random numbers in the range [lo, hi) @param lo lower limit of range @param hi upper limit of range @param d array of random numbers to be generated
[ "Generate", "n", "uniform", "random", "numbers", "in", "the", "range", "[", "lo", "hi", ")" ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/Random.java#L81-L89
17,357
haifengl/smile
math/src/main/java/smile/math/Random.java
Random.permutate
public int[] permutate(int n) { int[] x = new int[n]; for (int i = 0; i < n; i++) { x[i] = i; } permutate(x); return x; }
java
public int[] permutate(int n) { int[] x = new int[n]; for (int i = 0; i < n; i++) { x[i] = i; } permutate(x); return x; }
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Generates a permutation of 0, 1, 2, ..., n-1, which is useful for sampling without replacement.
[ "Generates", "a", "permutation", "of", "0", "1", "2", "...", "n", "-", "1", "which", "is", "useful", "for", "sampling", "without", "replacement", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/Random.java#L121-L130
17,358
haifengl/smile
core/src/main/java/smile/clustering/BIRCH.java
BIRCH.add
public void add(double[] x) { if (root == null) { root = new Node(); root.add(new Leaf(x)); root.update(x); } else { root.add(x); } }
java
public void add(double[] x) { if (root == null) { root = new Node(); root.add(new Leaf(x)); root.update(x); } else { root.add(x); } }
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Add a data point into CF tree.
[ "Add", "a", "data", "point", "into", "CF", "tree", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/clustering/BIRCH.java#L345-L353
17,359
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.setGraphics
public void setGraphics(java.awt.Graphics2D g2d) { this.g2d = g2d; // anti-aliasing methods g2d.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON); g2d.setRenderingHint(RenderingHints.KEY_RENDERING, RenderingHints.VALUE_RENDER_QUALITY); }
java
public void setGraphics(java.awt.Graphics2D g2d) { this.g2d = g2d; // anti-aliasing methods g2d.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON); g2d.setRenderingHint(RenderingHints.KEY_RENDERING, RenderingHints.VALUE_RENDER_QUALITY); }
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Set the Java2D graphics object.
[ "Set", "the", "Java2D", "graphics", "object", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L84-L89
17,360
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.clip
public void clip() { int x = (int) (projection.canvas.getWidth() * projection.canvas.margin); int y = (int) (projection.canvas.getHeight() * projection.canvas.margin); int w = (int) (projection.canvas.getWidth() * (1 - 2 * projection.canvas.margin)); int h = (int) (projection.canvas.getHeight() * (1 - 2 * projection.canvas.margin)); originalClip = g2d.getClip(); g2d.clipRect(x, y, w, h); }
java
public void clip() { int x = (int) (projection.canvas.getWidth() * projection.canvas.margin); int y = (int) (projection.canvas.getHeight() * projection.canvas.margin); int w = (int) (projection.canvas.getWidth() * (1 - 2 * projection.canvas.margin)); int h = (int) (projection.canvas.getHeight() * (1 - 2 * projection.canvas.margin)); originalClip = g2d.getClip(); g2d.clipRect(x, y, w, h); }
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Restrict the draw area to the valid base coordinate space.
[ "Restrict", "the", "draw", "area", "to", "the", "valid", "base", "coordinate", "space", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L168-L175
17,361
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.drawLine
private void drawLine(int[]... coord) { int[] x = new int[coord.length]; for (int i = 0; i < coord.length; i++) { x[i] = coord[i][0]; } int[] y = new int[coord.length]; for (int i = 0; i < coord.length; i++) { y[i] = coord[i][1]; } g2d.drawPolyline(x, y, coord.length); }
java
private void drawLine(int[]... coord) { int[] x = new int[coord.length]; for (int i = 0; i < coord.length; i++) { x[i] = coord[i][0]; } int[] y = new int[coord.length]; for (int i = 0; i < coord.length; i++) { y[i] = coord[i][1]; } g2d.drawPolyline(x, y, coord.length); }
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Draw poly line.
[ "Draw", "poly", "line", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L302-L314
17,362
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.drawLine
public void drawLine(double[]... coord) { int[][] sc = new int[coord.length][]; for (int i = 0; i < sc.length; i++) { sc[i] = projection.screenProjection(coord[i]); } drawLine(sc); }
java
public void drawLine(double[]... coord) { int[][] sc = new int[coord.length][]; for (int i = 0; i < sc.length; i++) { sc[i] = projection.screenProjection(coord[i]); } drawLine(sc); }
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Draw poly line. The coordinates are in logical coordinates.
[ "Draw", "poly", "line", ".", "The", "coordinates", "are", "in", "logical", "coordinates", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L319-L326
17,363
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.drawLineBaseRatio
public void drawLineBaseRatio(double[]... coord) { int[][] sc = new int[coord.length][]; for (int i = 0; i < sc.length; i++) { sc[i] = projection.screenProjectionBaseRatio(coord[i]); } drawLine(sc); }
java
public void drawLineBaseRatio(double[]... coord) { int[][] sc = new int[coord.length][]; for (int i = 0; i < sc.length; i++) { sc[i] = projection.screenProjectionBaseRatio(coord[i]); } drawLine(sc); }
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Draw poly line. The logical coordinates are proportional to the base coordinates.
[ "Draw", "poly", "line", ".", "The", "logical", "coordinates", "are", "proportional", "to", "the", "base", "coordinates", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L332-L339
17,364
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.drawPoint
public void drawPoint(char dot, double... coord) { int size = 2; int midSize = 3; int bigSize = 4; int[] sc = projection.screenProjection(coord); int x = sc[0]; int y = sc[1]; switch (dot) { case '+': g2d.drawLine(x - size, y, x + size, y); g2d.drawLine(x, y - size, x, y + size); break; case '-': g2d.drawLine(x - size, y, x + size, y); break; case '|': g2d.drawLine(x, y - size, x, y + size); break; case 'x': g2d.drawLine(x - size, y - size, x + size, y + size); g2d.drawLine(x + size, y - size, x - size, y + size); break; case '*': g2d.drawLine(x - bigSize, y, x + bigSize, y); g2d.drawLine(x, y - bigSize, x, y + bigSize); g2d.drawLine(x - midSize, y - midSize, x + midSize, y + midSize); g2d.drawLine(x + midSize, y - midSize, x - midSize, y + midSize); break; case 'o': g2d.drawOval(x - size, y - size, 2 * size, 2 * size); break; case 'O': g2d.drawOval(x - bigSize, y - bigSize, 2 * bigSize, 2 * bigSize); break; case '@': g2d.fillOval(x - size, y - size, 2 * size, 2 * size); break; case '#': g2d.fillOval(x - bigSize, y - bigSize, 2 * bigSize, 2 * bigSize); break; case 's': g2d.drawRect(x - size, y - size, 2 * size, 2 * size); break; case 'S': g2d.drawRect(x - bigSize, y - bigSize, 2 * bigSize, 2 * bigSize); break; case 'q': g2d.fillRect(x - size, y - size, 2 * size, 2 * size); break; case 'Q': g2d.fillRect(x - bigSize, y - bigSize, 2 * bigSize, 2 * bigSize); break; default: g2d.drawRect(x, y, 1, 1); break; } }
java
public void drawPoint(char dot, double... coord) { int size = 2; int midSize = 3; int bigSize = 4; int[] sc = projection.screenProjection(coord); int x = sc[0]; int y = sc[1]; switch (dot) { case '+': g2d.drawLine(x - size, y, x + size, y); g2d.drawLine(x, y - size, x, y + size); break; case '-': g2d.drawLine(x - size, y, x + size, y); break; case '|': g2d.drawLine(x, y - size, x, y + size); break; case 'x': g2d.drawLine(x - size, y - size, x + size, y + size); g2d.drawLine(x + size, y - size, x - size, y + size); break; case '*': g2d.drawLine(x - bigSize, y, x + bigSize, y); g2d.drawLine(x, y - bigSize, x, y + bigSize); g2d.drawLine(x - midSize, y - midSize, x + midSize, y + midSize); g2d.drawLine(x + midSize, y - midSize, x - midSize, y + midSize); break; case 'o': g2d.drawOval(x - size, y - size, 2 * size, 2 * size); break; case 'O': g2d.drawOval(x - bigSize, y - bigSize, 2 * bigSize, 2 * bigSize); break; case '@': g2d.fillOval(x - size, y - size, 2 * size, 2 * size); break; case '#': g2d.fillOval(x - bigSize, y - bigSize, 2 * bigSize, 2 * bigSize); break; case 's': g2d.drawRect(x - size, y - size, 2 * size, 2 * size); break; case 'S': g2d.drawRect(x - bigSize, y - bigSize, 2 * bigSize, 2 * bigSize); break; case 'q': g2d.fillRect(x - size, y - size, 2 * size, 2 * size); break; case 'Q': g2d.fillRect(x - bigSize, y - bigSize, 2 * bigSize, 2 * bigSize); break; default: g2d.drawRect(x, y, 1, 1); break; } }
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Draw a dot with given pattern. The coordinates are in logical coordinates. @param dot the pattern of dot: <ul> <li> . : dot <li> + : cross <li> - : - <li> | : | <li> * : star <li> x : x <li> o : circle <li> O : large circle <li> @ : solid circle <li> # : large sollid circle <li> s : square <li> S : large square <li> q : solid square <li> Q : large solid square </ul>
[ "Draw", "a", "dot", "with", "given", "pattern", ".", "The", "coordinates", "are", "in", "logical", "coordinates", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L368-L440
17,365
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.fillPolygon
public void fillPolygon(double[]... coord) { int[][] c = new int[coord.length][2]; for (int i = 0; i < coord.length; i++) { c[i] = projection.screenProjection(coord[i]); } int[] x = new int[c.length]; for (int i = 0; i < c.length; i++) { x[i] = c[i][0]; } int[] y = new int[c.length]; for (int i = 0; i < c.length; i++) { y[i] = c[i][1]; } g2d.fillPolygon(x, y, c.length); }
java
public void fillPolygon(double[]... coord) { int[][] c = new int[coord.length][2]; for (int i = 0; i < coord.length; i++) { c[i] = projection.screenProjection(coord[i]); } int[] x = new int[c.length]; for (int i = 0; i < c.length; i++) { x[i] = c[i][0]; } int[] y = new int[c.length]; for (int i = 0; i < c.length; i++) { y[i] = c[i][1]; } g2d.fillPolygon(x, y, c.length); }
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Fill polygon. The coordinates are in logical coordinates.
[ "Fill", "polygon", ".", "The", "coordinates", "are", "in", "logical", "coordinates", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L465-L481
17,366
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.fillPolygon
public void fillPolygon(float alpha, double[]... coord) { int[][] c = new int[coord.length][2]; for (int i = 0; i < coord.length; i++) { c[i] = projection.screenProjection(coord[i]); } int[] x = new int[c.length]; for (int i = 0; i < c.length; i++) { x[i] = c[i][0]; } int[] y = new int[c.length]; for (int i = 0; i < c.length; i++) { y[i] = c[i][1]; } Composite cs = g2d.getComposite(); g2d.setComposite(AlphaComposite.getInstance(AlphaComposite.SRC_OVER, alpha)); g2d.fillPolygon(x, y, c.length); g2d.setComposite(cs); }
java
public void fillPolygon(float alpha, double[]... coord) { int[][] c = new int[coord.length][2]; for (int i = 0; i < coord.length; i++) { c[i] = projection.screenProjection(coord[i]); } int[] x = new int[c.length]; for (int i = 0; i < c.length; i++) { x[i] = c[i][0]; } int[] y = new int[c.length]; for (int i = 0; i < c.length; i++) { y[i] = c[i][1]; } Composite cs = g2d.getComposite(); g2d.setComposite(AlphaComposite.getInstance(AlphaComposite.SRC_OVER, alpha)); g2d.fillPolygon(x, y, c.length); g2d.setComposite(cs); }
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Fill polygon. The coordinates are in logical coordinates. This also supports basic alpha compositing rules for combining source and destination colors to achieve blending and transparency effects with graphics and images. @param alpha the constant alpha to be multiplied with the alpha of the source. alpha must be a floating point number in the inclusive range [0.0, 1.0].
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L492-L513
17,367
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.drawRect
public void drawRect(double[] topLeft, double[] rightBottom) { if (!(projection instanceof Projection2D)) { throw new UnsupportedOperationException("Only 2D graphics supports drawing rectangles."); } int[] sc = projection.screenProjection(topLeft); int[] sc2 = projection.screenProjection(rightBottom); g2d.drawRect(sc[0], sc[1], sc2[0] - sc[0], sc2[1] - sc[1]); }
java
public void drawRect(double[] topLeft, double[] rightBottom) { if (!(projection instanceof Projection2D)) { throw new UnsupportedOperationException("Only 2D graphics supports drawing rectangles."); } int[] sc = projection.screenProjection(topLeft); int[] sc2 = projection.screenProjection(rightBottom); g2d.drawRect(sc[0], sc[1], sc2[0] - sc[0], sc2[1] - sc[1]); }
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Draw the outline of the specified rectangle.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L518-L527
17,368
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.drawRectBaseRatio
public void drawRectBaseRatio(double[] topLeft, double[] rightBottom) { if (!(projection instanceof Projection2D)) { throw new UnsupportedOperationException("Only 2D graphics supports drawing rectangles."); } int[] sc = projection.screenProjectionBaseRatio(topLeft); int[] sc2 = projection.screenProjectionBaseRatio(rightBottom); g2d.drawRect(sc[0], sc[1], sc2[0] - sc[0], sc2[1] - sc[1]); }
java
public void drawRectBaseRatio(double[] topLeft, double[] rightBottom) { if (!(projection instanceof Projection2D)) { throw new UnsupportedOperationException("Only 2D graphics supports drawing rectangles."); } int[] sc = projection.screenProjectionBaseRatio(topLeft); int[] sc2 = projection.screenProjectionBaseRatio(rightBottom); g2d.drawRect(sc[0], sc[1], sc2[0] - sc[0], sc2[1] - sc[1]); }
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Draw the outline of the specified rectangle. The logical coordinates are proportional to the base coordinates.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L533-L542
17,369
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.fillRectBaseRatio
public void fillRectBaseRatio(double[] topLeft, double[] rightBottom) { if (!(projection instanceof Projection2D)) { throw new UnsupportedOperationException("Only 2D graphics supports drawing rectangles."); } int[] sc = projection.screenProjectionBaseRatio(topLeft); int[] sc2 = projection.screenProjectionBaseRatio(rightBottom); g2d.fillRect(sc[0], sc[1], sc2[0] - sc[0], sc2[1] - sc[1]); }
java
public void fillRectBaseRatio(double[] topLeft, double[] rightBottom) { if (!(projection instanceof Projection2D)) { throw new UnsupportedOperationException("Only 2D graphics supports drawing rectangles."); } int[] sc = projection.screenProjectionBaseRatio(topLeft); int[] sc2 = projection.screenProjectionBaseRatio(rightBottom); g2d.fillRect(sc[0], sc[1], sc2[0] - sc[0], sc2[1] - sc[1]); }
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Fill the specified rectangle. The logical coordinates are proportional to the base coordinates.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L562-L571
17,370
haifengl/smile
plot/src/main/java/smile/plot/Graphics.java
Graphics.rotate
public void rotate(double x, double y) { if (!(projection instanceof Projection3D)) { throw new UnsupportedOperationException("Only 3D graphics supports rotation."); } ((Projection3D) projection).rotate(x, y); }
java
public void rotate(double x, double y) { if (!(projection instanceof Projection3D)) { throw new UnsupportedOperationException("Only 3D graphics supports rotation."); } ((Projection3D) projection).rotate(x, y); }
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Rotate the 3D view based on the changes on mouse position. @param x changes of mouse position on the x axis. @param y changes on mouse position on the y axis.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Graphics.java#L578-L584
17,371
haifengl/smile
core/src/main/java/smile/projection/GHA.java
GHA.learn
public double learn(double[] x) { if (x.length != n) { throw new IllegalArgumentException(String.format("Invalid input vector size: %d, expected: %d", x.length, n)); } projection.ax(x, y); for (int j = 0; j < p; j++) { for (int i = 0; i < n; i++) { double delta = x[i]; for (int l = 0; l <= j; l++) { delta -= projection.get(l, i) * y[l]; } projection.add(j, i, r * y[j] * delta); if (Double.isInfinite(projection.get(j, i))) { throw new IllegalStateException("GHA lost convergence. Lower learning rate?"); } } } projection.ax(x, y); projection.atx(y, wy); return Math.squaredDistance(x, wy); }
java
public double learn(double[] x) { if (x.length != n) { throw new IllegalArgumentException(String.format("Invalid input vector size: %d, expected: %d", x.length, n)); } projection.ax(x, y); for (int j = 0; j < p; j++) { for (int i = 0; i < n; i++) { double delta = x[i]; for (int l = 0; l <= j; l++) { delta -= projection.get(l, i) * y[l]; } projection.add(j, i, r * y[j] * delta); if (Double.isInfinite(projection.get(j, i))) { throw new IllegalStateException("GHA lost convergence. Lower learning rate?"); } } } projection.ax(x, y); projection.atx(y, wy); return Math.squaredDistance(x, wy); }
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Update the model with a new sample. @param x the centered learning sample whose E(x) = 0. @return the approximation error for input sample.
[ "Update", "the", "model", "with", "a", "new", "sample", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/projection/GHA.java#L182-L206
17,372
haifengl/smile
core/src/main/java/smile/classification/RBFNetwork.java
RBFNetwork.rep
private static RadialBasisFunction[] rep(RadialBasisFunction rbf, int k) { RadialBasisFunction[] arr = new RadialBasisFunction[k]; Arrays.fill(arr, rbf); return arr; }
java
private static RadialBasisFunction[] rep(RadialBasisFunction rbf, int k) { RadialBasisFunction[] arr = new RadialBasisFunction[k]; Arrays.fill(arr, rbf); return arr; }
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Returns an array of radial basis functions initialized with given values. @param rbf the initial value of array. @param k the size of array. @return an array of radial basis functions initialized with given values
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/classification/RBFNetwork.java#L254-L258
17,373
haifengl/smile
math/src/main/java/smile/math/matrix/SVD.java
SVD.getS
public DenseMatrix getS() { DenseMatrix S = Matrix.zeros(U.nrows(), V.nrows()); for (int i = 0; i < s.length; i++) { S.set(i, i, s[i]); } return S; }
java
public DenseMatrix getS() { DenseMatrix S = Matrix.zeros(U.nrows(), V.nrows()); for (int i = 0; i < s.length; i++) { S.set(i, i, s[i]); } return S; }
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Returns the diagonal matrix of singular values
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/SVD.java#L121-L129
17,374
haifengl/smile
math/src/main/java/smile/math/matrix/SVD.java
SVD.rank
public int rank() { if (!full) { throw new IllegalStateException("This is not a FULL singular value decomposition."); } int r = 0; for (int i = 0; i < s.length; i++) { if (s[i] > tol) { r++; } } return r; }
java
public int rank() { if (!full) { throw new IllegalStateException("This is not a FULL singular value decomposition."); } int r = 0; for (int i = 0; i < s.length; i++) { if (s[i] > tol) { r++; } } return r; }
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Returns the effective numerical matrix rank. The number of nonnegligible singular values.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/SVD.java#L142-L154
17,375
haifengl/smile
math/src/main/java/smile/math/matrix/SVD.java
SVD.nullity
public int nullity() { if (!full) { throw new IllegalStateException("This is not a FULL singular value decomposition."); } int r = 0; for (int i = 0; i < s.length; i++) { if (s[i] <= tol) { r++; } } return r; }
java
public int nullity() { if (!full) { throw new IllegalStateException("This is not a FULL singular value decomposition."); } int r = 0; for (int i = 0; i < s.length; i++) { if (s[i] <= tol) { r++; } } return r; }
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Returns the dimension of null space. The number of negligible singular values.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/SVD.java#L160-L172
17,376
haifengl/smile
math/src/main/java/smile/math/matrix/SVD.java
SVD.range
public DenseMatrix range() { if (!full) { throw new IllegalStateException("This is not a FULL singular value decomposition."); } int nr = 0; DenseMatrix rnge = Matrix.zeros(m, rank()); for (int j = 0; j < n; j++) { if (s[j] > tol) { for (int i = 0; i < m; i++) { rnge.set(i, nr, U.get(i, j)); } nr++; } } return rnge; }
java
public DenseMatrix range() { if (!full) { throw new IllegalStateException("This is not a FULL singular value decomposition."); } int nr = 0; DenseMatrix rnge = Matrix.zeros(m, rank()); for (int j = 0; j < n; j++) { if (s[j] > tol) { for (int i = 0; i < m; i++) { rnge.set(i, nr, U.get(i, j)); } nr++; } } return rnge; }
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Returns a matrix of which columns give an orthonormal basis for the range space.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/SVD.java#L200-L216
17,377
haifengl/smile
math/src/main/java/smile/math/matrix/SVD.java
SVD.nullspace
public DenseMatrix nullspace() { if (!full) { throw new IllegalStateException("This is not a FULL singular value decomposition."); } int nn = 0; DenseMatrix nullsp = Matrix.zeros(n, nullity()); for (int j = 0; j < n; j++) { if (s[j] <= tol) { for (int jj = 0; jj < n; jj++) { nullsp.set(jj, nn, V.get(jj, j)); } nn++; } } return nullsp; }
java
public DenseMatrix nullspace() { if (!full) { throw new IllegalStateException("This is not a FULL singular value decomposition."); } int nn = 0; DenseMatrix nullsp = Matrix.zeros(n, nullity()); for (int j = 0; j < n; j++) { if (s[j] <= tol) { for (int jj = 0; jj < n; jj++) { nullsp.set(jj, nn, V.get(jj, j)); } nn++; } } return nullsp; }
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Returns a matrix of which columns give an orthonormal basis for the null space.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/matrix/SVD.java#L221-L237
17,378
haifengl/smile
math/src/main/java/smile/math/IntArrayList.java
IntArrayList.ensureCapacity
public void ensureCapacity(int capacity) { if (capacity > data.length) { int newCap = Math.max(data.length << 1, capacity); int[] tmp = new int[newCap]; System.arraycopy(data, 0, tmp, 0, data.length); data = tmp; } }
java
public void ensureCapacity(int capacity) { if (capacity > data.length) { int newCap = Math.max(data.length << 1, capacity); int[] tmp = new int[newCap]; System.arraycopy(data, 0, tmp, 0, data.length); data = tmp; } }
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Increases the capacity, if necessary, to ensure that it can hold at least the number of values specified by the minimum capacity argument. @param capacity the desired minimum capacity.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/IntArrayList.java#L74-L81
17,379
haifengl/smile
math/src/main/java/smile/math/IntArrayList.java
IntArrayList.set
public IntArrayList set(int index, int val) { if (index < 0 || index >= size) { throw new IndexOutOfBoundsException(String.valueOf(index)); } data[index] = val; return this; }
java
public IntArrayList set(int index, int val) { if (index < 0 || index >= size) { throw new IndexOutOfBoundsException(String.valueOf(index)); } data[index] = val; return this; }
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Replaces the value at the specified position in this list with the specified value. @param index index of the value to replace @param val value to be stored at the specified position @throws IndexOutOfBoundsException if the index is out of range (index &lt; 0 || index &ge; size())
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/IntArrayList.java#L150-L156
17,380
haifengl/smile
math/src/main/java/smile/math/IntArrayList.java
IntArrayList.remove
public int remove(int index) { if (index < 0 || index >= size) { throw new IndexOutOfBoundsException(String.valueOf(index)); } int old = get(index); if (index == 0) { // data at the front System.arraycopy(data, 1, data, 0, size - 1); } else if (size - 1 == index) { // no copy to make, decrementing pos "deletes" values at // the end } else { // data in the middle System.arraycopy(data, index + 1, data, index, size - (index + 1)); } size--; return old; }
java
public int remove(int index) { if (index < 0 || index >= size) { throw new IndexOutOfBoundsException(String.valueOf(index)); } int old = get(index); if (index == 0) { // data at the front System.arraycopy(data, 1, data, 0, size - 1); } else if (size - 1 == index) { // no copy to make, decrementing pos "deletes" values at // the end } else { // data in the middle System.arraycopy(data, index + 1, data, index, size - (index + 1)); } size--; return old; }
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Removes the value at specified index from the list. @param index index of the element to remove. @return the value previously stored at specified index @throws IndexOutOfBoundsException if the index is out of range (index &lt; 0 || index &ge; size())
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/IntArrayList.java#L173-L193
17,381
haifengl/smile
interpolation/src/main/java/smile/interpolation/KrigingInterpolation.java
KrigingInterpolation.rdist
private double rdist(double[] x1, double[] x2) { double d = 0.0; for (int i = 0; i < x1.length; i++) { double t = x1[i] - x2[i]; d += t * t; } return Math.sqrt(d); }
java
private double rdist(double[] x1, double[] x2) { double d = 0.0; for (int i = 0; i < x1.length; i++) { double t = x1[i] - x2[i]; d += t * t; } return Math.sqrt(d); }
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Cartesian distance.
[ "Cartesian", "distance", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/interpolation/src/main/java/smile/interpolation/KrigingInterpolation.java#L160-L167
17,382
haifengl/smile
math/src/main/java/smile/math/Histogram.java
Histogram.histogram
public static double[][] histogram(int[] data, double[] breaks) { int k = breaks.length - 1; if (k <= 1) { throw new IllegalArgumentException("Invalid number of bins: " + k); } double[][] freq = new double[3][k]; for (int i = 0; i < k; i++) { freq[0][i] = breaks[i]; freq[1][i] = breaks[i + 1]; freq[2][i] = 0; } for (int d : data) { int j = Arrays.binarySearch(breaks, d); if (j >= k) { j = k - 1; } if (j < -1 && j >= -breaks.length) { j = -j - 2; } if (j >= 0) { freq[2][j]++; } } return freq; }
java
public static double[][] histogram(int[] data, double[] breaks) { int k = breaks.length - 1; if (k <= 1) { throw new IllegalArgumentException("Invalid number of bins: " + k); } double[][] freq = new double[3][k]; for (int i = 0; i < k; i++) { freq[0][i] = breaks[i]; freq[1][i] = breaks[i + 1]; freq[2][i] = 0; } for (int d : data) { int j = Arrays.binarySearch(breaks, d); if (j >= k) { j = k - 1; } if (j < -1 && j >= -breaks.length) { j = -j - 2; } if (j >= 0) { freq[2][j]++; } } return freq; }
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Generate the histogram of n bins. @param data the data points. @param breaks an array of size k+1 giving the breakpoints between histogram cells. Must be in ascending order. @return a 3-by-n bins array of which first row is the lower bound of bins, second row is the upper bound of bins, and the third row is the frequence count.
[ "Generate", "the", "histogram", "of", "n", "bins", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/Histogram.java#L131-L161
17,383
haifengl/smile
math/src/main/java/smile/math/Histogram.java
Histogram.breaks
public static double[] breaks(double[] x, double h) { return breaks(Math.min(x), Math.max(x), h); }
java
public static double[] breaks(double[] x, double h) { return breaks(Math.min(x), Math.max(x), h); }
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Returns the breakpoints between histogram cells for a dataset based on a suggested bin width h. @param x the data set. @param h the bin width. @return the breakpoints between histogram cells
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/Histogram.java#L310-L312
17,384
haifengl/smile
math/src/main/java/smile/math/Histogram.java
Histogram.breaks
public static double[] breaks(double min, double max, double h) { if (h <= 0.0) { throw new IllegalArgumentException("Invalid bin width: " + h); } if (min > max) { throw new IllegalArgumentException("Invalid lower and upper bounds: " + min + " > " + max); } int k = (int) Math.ceil((max-min) / h); double[] breaks = new double[k + 1]; breaks[0] = min - (h * k - (max - min)) / 2; breaks[k] = max + (h * k - (max - min)) / 2; for (int i = 1; i < k; i++) { breaks[i] = breaks[i - 1] + h; } return breaks; }
java
public static double[] breaks(double min, double max, double h) { if (h <= 0.0) { throw new IllegalArgumentException("Invalid bin width: " + h); } if (min > max) { throw new IllegalArgumentException("Invalid lower and upper bounds: " + min + " > " + max); } int k = (int) Math.ceil((max-min) / h); double[] breaks = new double[k + 1]; breaks[0] = min - (h * k - (max - min)) / 2; breaks[k] = max + (h * k - (max - min)) / 2; for (int i = 1; i < k; i++) { breaks[i] = breaks[i - 1] + h; } return breaks; }
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Returns the breakpoints between histogram cells for a given range based on a suggested bin width h. @param min the lower bound of bins. @param max the upper bound of bins. @param h the bin width. @return the breakpoints between histogram cells
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/Histogram.java#L322-L341
17,385
haifengl/smile
math/src/main/java/smile/math/Histogram.java
Histogram.breaks
public static double[] breaks(double[] x, int k) { return breaks(Math.min(x), Math.max(x), k); }
java
public static double[] breaks(double[] x, int k) { return breaks(Math.min(x), Math.max(x), k); }
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Returns the breakpoints between histogram cells for a dataset. @param x the data set. @param k the number of bins. @return the breakpoints between histogram cells
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/Histogram.java#L349-L351
17,386
haifengl/smile
math/src/main/java/smile/math/Histogram.java
Histogram.breaks
public static double[] breaks(double min, double max, int k) { if (k <= 1) { throw new IllegalArgumentException("Invalid number of bins: " + k); } if (min > max) { throw new IllegalArgumentException("Invalid lower and upper bounds: " + min + " > " + max); } double h = (max - min) / k; return breaks(min, max, h); }
java
public static double[] breaks(double min, double max, int k) { if (k <= 1) { throw new IllegalArgumentException("Invalid number of bins: " + k); } if (min > max) { throw new IllegalArgumentException("Invalid lower and upper bounds: " + min + " > " + max); } double h = (max - min) / k; return breaks(min, max, h); }
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Returns the breakpoints between histogram cells for a given range. @param min the lower bound of bins. @param max the upper bound of bins. @param k the number of bins. @return the breakpoints between histogram cells
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/Histogram.java#L360-L371
17,387
haifengl/smile
math/src/main/java/smile/math/Histogram.java
Histogram.bins
public static int bins(double[] x, double h) { if (h <= 0.0) { throw new IllegalArgumentException("Invalid bin width: " + h); } double max = Math.max(x); double min = Math.min(x); return (int) Math.ceil((max-min) / h); }
java
public static int bins(double[] x, double h) { if (h <= 0.0) { throw new IllegalArgumentException("Invalid bin width: " + h); } double max = Math.max(x); double min = Math.min(x); return (int) Math.ceil((max-min) / h); }
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Returns the number of bins for a data based on a suggested bin width h. @param x the data set. @param h the bin width. @return the number of bins k = ceil((max - min) / h)
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/math/Histogram.java#L379-L388
17,388
haifengl/smile
core/src/main/java/smile/feature/GAFeatureSelection.java
GAFeatureSelection.learn
public BitString[] learn(int size, int generation, ClassifierTrainer<double[]> trainer, ClassificationMeasure measure, double[][] x, int[] y, int k) { if (size <= 0) { throw new IllegalArgumentException("Invalid population size: " + size); } if (k < 2) { throw new IllegalArgumentException("Invalid k-fold cross validation: " + k); } if (x.length != y.length) { throw new IllegalArgumentException(String.format("The sizes of X and Y don't match: %d != %d", x.length, y.length)); } int p = x[0].length; ClassificationFitness fitness = new ClassificationFitness(trainer, measure, x, y, k); BitString[] seeds = new BitString[size]; for (int i = 0; i < size; i++) { seeds[i] = new BitString(p, fitness, crossover, crossoverRate, mutationRate); } GeneticAlgorithm<BitString> ga = new GeneticAlgorithm<>(seeds, selection); ga.evolve(generation); return seeds; }
java
public BitString[] learn(int size, int generation, ClassifierTrainer<double[]> trainer, ClassificationMeasure measure, double[][] x, int[] y, int k) { if (size <= 0) { throw new IllegalArgumentException("Invalid population size: " + size); } if (k < 2) { throw new IllegalArgumentException("Invalid k-fold cross validation: " + k); } if (x.length != y.length) { throw new IllegalArgumentException(String.format("The sizes of X and Y don't match: %d != %d", x.length, y.length)); } int p = x[0].length; ClassificationFitness fitness = new ClassificationFitness(trainer, measure, x, y, k); BitString[] seeds = new BitString[size]; for (int i = 0; i < size; i++) { seeds[i] = new BitString(p, fitness, crossover, crossoverRate, mutationRate); } GeneticAlgorithm<BitString> ga = new GeneticAlgorithm<>(seeds, selection); ga.evolve(generation); return seeds; }
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Genetic algorithm based feature selection for classification. @param size the population size of Genetic Algorithm. @param generation the maximum number of iterations. @param trainer classifier trainer. @param measure classification measure as the chromosome fitness measure. @param x training instances. @param y training labels. @param k k-fold cross validation for the evaluation. @return bit strings of last generation.
[ "Genetic", "algorithm", "based", "feature", "selection", "for", "classification", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/feature/GAFeatureSelection.java#L107-L132
17,389
haifengl/smile
data/src/main/java/smile/data/NominalAttribute.java
NominalAttribute.valueOf
@Override public double valueOf(String s) throws ParseException { Integer i = map.get(s); if (i == null) { if (open) { i = values.size(); map.put(s, i); values.add(s); } else { throw new ParseException("Invalid string value: " + s, 0); } } return i; }
java
@Override public double valueOf(String s) throws ParseException { Integer i = map.get(s); if (i == null) { if (open) { i = values.size(); map.put(s, i); values.add(s); } else { throw new ParseException("Invalid string value: " + s, 0); } } return i; }
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Returns the ordinal value of a string value.
[ "Returns", "the", "ordinal", "value", "of", "a", "string", "value", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/data/src/main/java/smile/data/NominalAttribute.java#L164-L178
17,390
haifengl/smile
plot/src/main/java/smile/plot/Projection.java
Projection.initBaseCoordsProjection
private void initBaseCoordsProjection() { baseScreenCoords = new int[canvas.base.baseCoords.length][2]; for (int i = 0; i < canvas.base.dimension + 1; i++) { double[] ratio = baseCoordsScreenProjectionRatio(canvas.base.baseCoords[i]); baseScreenCoords[i][0] = (int) (canvas.getWidth() * (canvas.margin + (1 - 2 * canvas.margin) * ratio[0])); baseScreenCoords[i][1] = (int) (canvas.getHeight() - canvas.getHeight() * (canvas.margin + (1 - 2 * canvas.margin) * ratio[1])); } }
java
private void initBaseCoordsProjection() { baseScreenCoords = new int[canvas.base.baseCoords.length][2]; for (int i = 0; i < canvas.base.dimension + 1; i++) { double[] ratio = baseCoordsScreenProjectionRatio(canvas.base.baseCoords[i]); baseScreenCoords[i][0] = (int) (canvas.getWidth() * (canvas.margin + (1 - 2 * canvas.margin) * ratio[0])); baseScreenCoords[i][1] = (int) (canvas.getHeight() - canvas.getHeight() * (canvas.margin + (1 - 2 * canvas.margin) * ratio[1])); } }
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Initialize base coordinates on Java2D screen.
[ "Initialize", "base", "coordinates", "on", "Java2D", "screen", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Projection.java#L55-L62
17,391
haifengl/smile
plot/src/main/java/smile/plot/Projection.java
Projection.screenProjection
public int[] screenProjection(double... coord) { double[] sc = new double[2]; sc[0] = baseScreenCoords[0][0]; sc[1] = baseScreenCoords[0][1]; for (int i = 0; i < canvas.base.dimension; i++) { sc[0] += ((coord[i] - canvas.base.baseCoords[0][i]) / (canvas.base.baseCoords[i + 1][i] - canvas.base.baseCoords[0][i])) * (baseScreenCoords[i + 1][0] - baseScreenCoords[0][0]); sc[1] += ((coord[i] - canvas.base.baseCoords[0][i]) / (canvas.base.baseCoords[i + 1][i] - canvas.base.baseCoords[0][i])) * (baseScreenCoords[i + 1][1] - baseScreenCoords[0][1]); } return new int[]{(int) sc[0], (int) sc[1]}; }
java
public int[] screenProjection(double... coord) { double[] sc = new double[2]; sc[0] = baseScreenCoords[0][0]; sc[1] = baseScreenCoords[0][1]; for (int i = 0; i < canvas.base.dimension; i++) { sc[0] += ((coord[i] - canvas.base.baseCoords[0][i]) / (canvas.base.baseCoords[i + 1][i] - canvas.base.baseCoords[0][i])) * (baseScreenCoords[i + 1][0] - baseScreenCoords[0][0]); sc[1] += ((coord[i] - canvas.base.baseCoords[0][i]) / (canvas.base.baseCoords[i + 1][i] - canvas.base.baseCoords[0][i])) * (baseScreenCoords[i + 1][1] - baseScreenCoords[0][1]); } return new int[]{(int) sc[0], (int) sc[1]}; }
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Project logical coordinates to Java2D coordinates.
[ "Project", "logical", "coordinates", "to", "Java2D", "coordinates", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Projection.java#L67-L78
17,392
haifengl/smile
plot/src/main/java/smile/plot/Projection.java
Projection.screenProjectionBaseRatio
public int[] screenProjectionBaseRatio(double... coord) { double[] sc = new double[2]; sc[0] = baseScreenCoords[0][0]; sc[1] = baseScreenCoords[0][1]; for (int i = 0; i < canvas.base.dimension; i++) { sc[0] += coord[i] * (baseScreenCoords[i + 1][0] - baseScreenCoords[0][0]); sc[1] += coord[i] * (baseScreenCoords[i + 1][1] - baseScreenCoords[0][1]); } return new int[]{(int) sc[0], (int) sc[1]}; }
java
public int[] screenProjectionBaseRatio(double... coord) { double[] sc = new double[2]; sc[0] = baseScreenCoords[0][0]; sc[1] = baseScreenCoords[0][1]; for (int i = 0; i < canvas.base.dimension; i++) { sc[0] += coord[i] * (baseScreenCoords[i + 1][0] - baseScreenCoords[0][0]); sc[1] += coord[i] * (baseScreenCoords[i + 1][1] - baseScreenCoords[0][1]); } return new int[]{(int) sc[0], (int) sc[1]}; }
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Project logical coordinates in base ratio to Java2D coordinates.
[ "Project", "logical", "coordinates", "in", "base", "ratio", "to", "Java2D", "coordinates", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Projection.java#L83-L94
17,393
haifengl/smile
math/src/main/java/smile/stat/distribution/AbstractDistribution.java
AbstractDistribution.logLikelihood
@Override public double logLikelihood(double[] x) { double L = 0.0; for (double xi : x) L += logp(xi); return L; }
java
@Override public double logLikelihood(double[] x) { double L = 0.0; for (double xi : x) L += logp(xi); return L; }
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The likelihood given a sample set following the distribution.
[ "The", "likelihood", "given", "a", "sample", "set", "following", "the", "distribution", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/math/src/main/java/smile/stat/distribution/AbstractDistribution.java#L125-L133
17,394
haifengl/smile
core/src/main/java/smile/util/MulticoreExecutor.java
MulticoreExecutor.createThreadPool
private static void createThreadPool() { if (nprocs == -1) { int n = -1; try { String env = System.getProperty("smile.threads"); if (env != null) { n = Integer.parseInt(env); } } catch (Exception ex) { logger.error("Failed to create multi-core execution thread pool", ex); } if (n < 1) { nprocs = Runtime.getRuntime().availableProcessors(); } else { nprocs = n; } if (nprocs > 1) { threads = (ThreadPoolExecutor) Executors.newFixedThreadPool(nprocs, new SimpleDeamonThreadFactory()); } } }
java
private static void createThreadPool() { if (nprocs == -1) { int n = -1; try { String env = System.getProperty("smile.threads"); if (env != null) { n = Integer.parseInt(env); } } catch (Exception ex) { logger.error("Failed to create multi-core execution thread pool", ex); } if (n < 1) { nprocs = Runtime.getRuntime().availableProcessors(); } else { nprocs = n; } if (nprocs > 1) { threads = (ThreadPoolExecutor) Executors.newFixedThreadPool(nprocs, new SimpleDeamonThreadFactory()); } } }
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Creates the worker thread pool.
[ "Creates", "the", "worker", "thread", "pool", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/util/MulticoreExecutor.java#L52-L74
17,395
haifengl/smile
core/src/main/java/smile/util/MulticoreExecutor.java
MulticoreExecutor.run
public static <T> List<T> run(Collection<? extends Callable<T>> tasks) throws Exception { createThreadPool(); List<T> results = new ArrayList<>(); if (threads == null) { for (Callable<T> task : tasks) { results.add(task.call()); } } else { if (threads.getActiveCount() < nprocs) { List<Future<T>> futures = threads.invokeAll(tasks); for (Future<T> future : futures) { results.add(future.get()); } } else { // Thread pool is busy. Just run in the caller's thread. for (Callable<T> task : tasks) { results.add(task.call()); } } } return results; }
java
public static <T> List<T> run(Collection<? extends Callable<T>> tasks) throws Exception { createThreadPool(); List<T> results = new ArrayList<>(); if (threads == null) { for (Callable<T> task : tasks) { results.add(task.call()); } } else { if (threads.getActiveCount() < nprocs) { List<Future<T>> futures = threads.invokeAll(tasks); for (Future<T> future : futures) { results.add(future.get()); } } else { // Thread pool is busy. Just run in the caller's thread. for (Callable<T> task : tasks) { results.add(task.call()); } } } return results; }
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Executes the given tasks serially or parallel depending on the number of cores of the system. Returns a list of result objects of each task. The results of this method are undefined if the given collection is modified while this operation is in progress. @param tasks the collection of tasks. @return a list of result objects in the same sequential order as produced by the iterator for the given task list. @throws Exception if unable to compute a result.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/util/MulticoreExecutor.java#L95-L118
17,396
haifengl/smile
plot/src/main/java/smile/plot/Histogram.java
Histogram.plot
public static PlotCanvas plot(String id, double[] data) { Histogram histogram = new Histogram(data); histogram.setID(id); double[] lowerBound = {Math.min(data), 0}; double[] upperBound = {Math.max(data), 0}; double[][] freq = histogram.getHistogram(); for (int i = 0; i < freq.length; i++) { if (freq[i][1] > upperBound[1]) { upperBound[1] = freq[i][1]; } } PlotCanvas canvas = new PlotCanvas(lowerBound, upperBound); canvas.getAxis(0).setGridVisible(false); canvas.add(histogram); return canvas; }
java
public static PlotCanvas plot(String id, double[] data) { Histogram histogram = new Histogram(data); histogram.setID(id); double[] lowerBound = {Math.min(data), 0}; double[] upperBound = {Math.max(data), 0}; double[][] freq = histogram.getHistogram(); for (int i = 0; i < freq.length; i++) { if (freq[i][1] > upperBound[1]) { upperBound[1] = freq[i][1]; } } PlotCanvas canvas = new PlotCanvas(lowerBound, upperBound); canvas.getAxis(0).setGridVisible(false); canvas.add(histogram); return canvas; }
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Create a plot canvas with the histogram plot. @param id the id of the plot. @param data a sample set.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/plot/src/main/java/smile/plot/Histogram.java#L335-L354
17,397
haifengl/smile
core/src/main/java/smile/association/FPGrowth.java
FPGrowth.buildTotalSupportTree
TotalSupportTree buildTotalSupportTree() { TotalSupportTree ttree = new TotalSupportTree(minSupport, T0.numFreqItems, T0.order); learn(null, null, ttree); return ttree; }
java
TotalSupportTree buildTotalSupportTree() { TotalSupportTree ttree = new TotalSupportTree(minSupport, T0.numFreqItems, T0.order); learn(null, null, ttree); return ttree; }
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Mines the frequent item sets. The discovered frequent item sets will be stored in a total support tree.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/association/FPGrowth.java#L164-L168
17,398
haifengl/smile
core/src/main/java/smile/association/FPGrowth.java
FPGrowth.learn
private long learn(PrintStream out, List<ItemSet> list, TotalSupportTree ttree) { if (MulticoreExecutor.getThreadPoolSize() > 1) { return grow(out, list, ttree, T0, null, null, null); } else { return grow(out, list, ttree, T0, null); } }
java
private long learn(PrintStream out, List<ItemSet> list, TotalSupportTree ttree) { if (MulticoreExecutor.getThreadPoolSize() > 1) { return grow(out, list, ttree, T0, null, null, null); } else { return grow(out, list, ttree, T0, null); } }
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Mines the frequent item sets. The discovered frequent item sets will be printed out to the provided stream. @param out a print stream for output of frequent item sets. @return the number of discovered frequent item sets.
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e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/association/FPGrowth.java#L176-L182
17,399
haifengl/smile
core/src/main/java/smile/association/FPGrowth.java
FPGrowth.collect
private void collect(PrintStream out, List<ItemSet> list, TotalSupportTree ttree, int[] itemset, int support) { if (list != null) { synchronized (list) { list.add(new ItemSet(itemset, support)); } } if (out != null) { synchronized (out) { for (int i = 0; i < itemset.length; i++) { out.format("%d ", itemset[i]); } out.format("(%d)%n", support); } } if (ttree != null) { synchronized (ttree) { ttree.add(itemset, support); } } }
java
private void collect(PrintStream out, List<ItemSet> list, TotalSupportTree ttree, int[] itemset, int support) { if (list != null) { synchronized (list) { list.add(new ItemSet(itemset, support)); } } if (out != null) { synchronized (out) { for (int i = 0; i < itemset.length; i++) { out.format("%d ", itemset[i]); } out.format("(%d)%n", support); } } if (ttree != null) { synchronized (ttree) { ttree.add(itemset, support); } } }
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Adds an item set to the result.
[ "Adds", "an", "item", "set", "to", "the", "result", "." ]
e27e43e90fbaacce3f99d30120cf9dd6a764c33d
https://github.com/haifengl/smile/blob/e27e43e90fbaacce3f99d30120cf9dd6a764c33d/core/src/main/java/smile/association/FPGrowth.java#L322-L341