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class sklearn.neighbors.NearestCentroid(metric='euclidean', *, shrink_threshold=None) [source]
Nearest centroid classifier. Each class is represented by its centroid, with test samples classified to the class with the nearest centroid. Read more in the User Guide. Parameters
metricstr or callable
The metric to ... | sklearn.modules.generated.sklearn.neighbors.nearestcentroid#sklearn.neighbors.NearestCentroid |
sklearn.neighbors.NearestCentroid
class sklearn.neighbors.NearestCentroid(metric='euclidean', *, shrink_threshold=None) [source]
Nearest centroid classifier. Each class is represented by its centroid, with test samples classified to the class with the nearest centroid. Read more in the User Guide. Parameters
me... | sklearn.modules.generated.sklearn.neighbors.nearestcentroid |
fit(X, y) [source]
Fit the NearestCentroid model according to the given training data. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features. Note that centroid shrinking cannot be used with spa... | sklearn.modules.generated.sklearn.neighbors.nearestcentroid#sklearn.neighbors.NearestCentroid.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.neighbors.nearestcentroid#sklearn.neighbors.NearestCentroid.get_params |
predict(X) [source]
Perform classification on an array of test vectors X. The predicted class C for each sample in X is returned. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Cndarray of shape (n_samples,)
Notes If the metric constructor parameter is “precomputed”, X is assumed to be ... | sklearn.modules.generated.sklearn.neighbors.nearestcentroid#sklearn.neighbors.NearestCentroid.predict |
score(X, y, sample_weight=None) [source]
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_featur... | sklearn.modules.generated.sklearn.neighbors.nearestcentroid#sklearn.neighbors.NearestCentroid.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.neighbors.nearestcentroid#sklearn.neighbors.NearestCentroid.set_params |
class sklearn.neighbors.NearestNeighbors(*, n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None) [source]
Unsupervised learner for implementing neighbor searches. Read more in the User Guide. New in version 0.9. Parameters
n_neighborsint, default=5... | sklearn.modules.generated.sklearn.neighbors.nearestneighbors#sklearn.neighbors.NearestNeighbors |
sklearn.neighbors.NearestNeighbors
class sklearn.neighbors.NearestNeighbors(*, n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None) [source]
Unsupervised learner for implementing neighbor searches. Read more in the User Guide. New in version 0.9. Pa... | sklearn.modules.generated.sklearn.neighbors.nearestneighbors |
fit(X, y=None) [source]
Fit the nearest neighbors estimator from the training dataset. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric=’precomputed’
Training data.
yIgnored
Not used, present for API consistency by convention. Returns
selfNea... | sklearn.modules.generated.sklearn.neighbors.nearestneighbors#sklearn.neighbors.NearestNeighbors.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.neighbors.nearestneighbors#sklearn.neighbors.NearestNeighbors.get_params |
kneighbors(X=None, n_neighbors=None, return_distance=True) [source]
Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point. Parameters
Xarray-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’, default=None
The query point or poin... | sklearn.modules.generated.sklearn.neighbors.nearestneighbors#sklearn.neighbors.NearestNeighbors.kneighbors |
kneighbors_graph(X=None, n_neighbors=None, mode='connectivity') [source]
Computes the (weighted) graph of k-Neighbors for points in X Parameters
Xarray-like of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’, default=None
The query point or points. If not provided, neighbors ... | sklearn.modules.generated.sklearn.neighbors.nearestneighbors#sklearn.neighbors.NearestNeighbors.kneighbors_graph |
radius_neighbors(X=None, radius=None, return_distance=True, sort_results=False) [source]
Finds the neighbors within a given radius of a point or points. Return the indices and distances of each point from the dataset lying in a ball with size radius around the points of the query array. Points lying on the boundary a... | sklearn.modules.generated.sklearn.neighbors.nearestneighbors#sklearn.neighbors.NearestNeighbors.radius_neighbors |
radius_neighbors_graph(X=None, radius=None, mode='connectivity', sort_results=False) [source]
Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Parameters
Xarray-like of shape (n_samples, n_features), default=None
The query point ... | sklearn.modules.generated.sklearn.neighbors.nearestneighbors#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.neighbors.nearestneighbors#sklearn.neighbors.NearestNeighbors.set_params |
class sklearn.neighbors.NeighborhoodComponentsAnalysis(n_components=None, *, init='auto', warm_start=False, max_iter=50, tol=1e-05, callback=None, verbose=0, random_state=None) [source]
Neighborhood Components Analysis Neighborhood Component Analysis (NCA) is a machine learning algorithm for metric learning. It learn... | sklearn.modules.generated.sklearn.neighbors.neighborhoodcomponentsanalysis#sklearn.neighbors.NeighborhoodComponentsAnalysis |
sklearn.neighbors.NeighborhoodComponentsAnalysis
class sklearn.neighbors.NeighborhoodComponentsAnalysis(n_components=None, *, init='auto', warm_start=False, max_iter=50, tol=1e-05, callback=None, verbose=0, random_state=None) [source]
Neighborhood Components Analysis Neighborhood Component Analysis (NCA) is a machi... | sklearn.modules.generated.sklearn.neighbors.neighborhoodcomponentsanalysis |
fit(X, y) [source]
Fit the model according to the given training data. Parameters
Xarray-like of shape (n_samples, n_features)
The training samples.
yarray-like of shape (n_samples,)
The corresponding training labels. Returns
selfobject
returns a trained NeighborhoodComponentsAnalysis model. | sklearn.modules.generated.sklearn.neighbors.neighborhoodcomponentsanalysis#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.neighbors.neighborhoodcomponentsanalysis#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.neighbors.neighborhoodcomponentsanalysis#sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.neighbors.neighborhoodcomponentsanalysis#sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params |
transform(X) [source]
Applies the learned transformation to the given data. Parameters
Xarray-like of shape (n_samples, n_features)
Data samples. Returns
X_embedded: ndarray of shape (n_samples, n_components)
The data samples transformed. Raises
NotFittedError
If fit has not been called before. | sklearn.modules.generated.sklearn.neighbors.neighborhoodcomponentsanalysis#sklearn.neighbors.NeighborhoodComponentsAnalysis.transform |
class sklearn.neighbors.RadiusNeighborsClassifier(radius=1.0, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', outlier_label=None, metric_params=None, n_jobs=None, **kwargs) [source]
Classifier implementing a vote among neighbors within a given radius Read more in the User Guide. Parame... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier#sklearn.neighbors.RadiusNeighborsClassifier |
sklearn.neighbors.RadiusNeighborsClassifier
class sklearn.neighbors.RadiusNeighborsClassifier(radius=1.0, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', outlier_label=None, metric_params=None, n_jobs=None, **kwargs) [source]
Classifier implementing a vote among neighbors within a giv... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier |
fit(X, y) [source]
Fit the radius neighbors classifier from the training dataset. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric=’precomputed’
Training data.
y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs)
Target valu... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier#sklearn.neighbors.RadiusNeighborsClassifier.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier#sklearn.neighbors.RadiusNeighborsClassifier.get_params |
predict(X) [source]
Predict the class labels for the provided data. Parameters
Xarray-like of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’
Test samples. Returns
yndarray of shape (n_queries,) or (n_queries, n_outputs)
Class labels for each data sample. | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier#sklearn.neighbors.RadiusNeighborsClassifier.predict |
predict_proba(X) [source]
Return probability estimates for the test data X. Parameters
Xarray-like of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’
Test samples. Returns
pndarray of shape (n_queries, n_classes), or a list of n_outputs
of such arrays if n_outputs > 1... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier#sklearn.neighbors.RadiusNeighborsClassifier.predict_proba |
radius_neighbors(X=None, radius=None, return_distance=True, sort_results=False) [source]
Finds the neighbors within a given radius of a point or points. Return the indices and distances of each point from the dataset lying in a ball with size radius around the points of the query array. Points lying on the boundary a... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier#sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors |
radius_neighbors_graph(X=None, radius=None, mode='connectivity', sort_results=False) [source]
Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Parameters
Xarray-like of shape (n_samples, n_features), default=None
The query point ... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier#sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors_graph |
score(X, y, sample_weight=None) [source]
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_featur... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier#sklearn.neighbors.RadiusNeighborsClassifier.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsclassifier#sklearn.neighbors.RadiusNeighborsClassifier.set_params |
class sklearn.neighbors.RadiusNeighborsRegressor(radius=1.0, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, **kwargs) [source]
Regression based on neighbors within a fixed radius. The target is predicted by local interpolation of the targets associated ... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsregressor#sklearn.neighbors.RadiusNeighborsRegressor |
sklearn.neighbors.RadiusNeighborsRegressor
class sklearn.neighbors.RadiusNeighborsRegressor(radius=1.0, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, **kwargs) [source]
Regression based on neighbors within a fixed radius. The target is predicted by l... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsregressor |
fit(X, y) [source]
Fit the radius neighbors regressor from the training dataset. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric=’precomputed’
Training data.
y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs)
Target value... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsregressor#sklearn.neighbors.RadiusNeighborsRegressor.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.neighbors.radiusneighborsregressor#sklearn.neighbors.RadiusNeighborsRegressor.get_params |
predict(X) [source]
Predict the target for the provided data Parameters
Xarray-like of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’
Test samples. Returns
yndarray of shape (n_queries,) or (n_queries, n_outputs), dtype=double
Target values. | sklearn.modules.generated.sklearn.neighbors.radiusneighborsregressor#sklearn.neighbors.RadiusNeighborsRegressor.predict |
radius_neighbors(X=None, radius=None, return_distance=True, sort_results=False) [source]
Finds the neighbors within a given radius of a point or points. Return the indices and distances of each point from the dataset lying in a ball with size radius around the points of the query array. Points lying on the boundary a... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsregressor#sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors |
radius_neighbors_graph(X=None, radius=None, mode='connectivity', sort_results=False) [source]
Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Parameters
Xarray-like of shape (n_samples, n_features), default=None
The query point ... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsregressor#sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors_graph |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsregressor#sklearn.neighbors.RadiusNeighborsRegressor.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.neighbors.radiusneighborsregressor#sklearn.neighbors.RadiusNeighborsRegressor.set_params |
class sklearn.neighbors.RadiusNeighborsTransformer(*, mode='distance', radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=1) [source]
Transform X into a (weighted) graph of neighbors nearer than a radius The transformed data is a sparse graph as returned by radius_neighbor... | sklearn.modules.generated.sklearn.neighbors.radiusneighborstransformer#sklearn.neighbors.RadiusNeighborsTransformer |
sklearn.neighbors.RadiusNeighborsTransformer
class sklearn.neighbors.RadiusNeighborsTransformer(*, mode='distance', radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=1) [source]
Transform X into a (weighted) graph of neighbors nearer than a radius The transformed data i... | sklearn.modules.generated.sklearn.neighbors.radiusneighborstransformer |
fit(X, y=None) [source]
Fit the radius neighbors transformer from the training dataset. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric=’precomputed’
Training data. Returns
selfRadiusNeighborsTransformer
The fitted radius neighbors transformer... | sklearn.modules.generated.sklearn.neighbors.radiusneighborstransformer#sklearn.neighbors.RadiusNeighborsTransformer.fit |
fit_transform(X, y=None) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Training set.
yignored
Returns
Xtsparse matrix of shape (n_samples, n_samples)... | sklearn.modules.generated.sklearn.neighbors.radiusneighborstransformer#sklearn.neighbors.RadiusNeighborsTransformer.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.neighbors.radiusneighborstransformer#sklearn.neighbors.RadiusNeighborsTransformer.get_params |
radius_neighbors(X=None, radius=None, return_distance=True, sort_results=False) [source]
Finds the neighbors within a given radius of a point or points. Return the indices and distances of each point from the dataset lying in a ball with size radius around the points of the query array. Points lying on the boundary a... | sklearn.modules.generated.sklearn.neighbors.radiusneighborstransformer#sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors |
radius_neighbors_graph(X=None, radius=None, mode='connectivity', sort_results=False) [source]
Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Parameters
Xarray-like of shape (n_samples, n_features), default=None
The query point ... | sklearn.modules.generated.sklearn.neighbors.radiusneighborstransformer#sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors_graph |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.neighbors.radiusneighborstransformer#sklearn.neighbors.RadiusNeighborsTransformer.set_params |
transform(X) [source]
Computes the (weighted) graph of Neighbors for points in X Parameters
Xarray-like of shape (n_samples_transform, n_features)
Sample data Returns
Xtsparse matrix of shape (n_samples_transform, n_samples_fit)
Xt[i, j] is assigned the weight of edge that connects i to j. Only the neig... | sklearn.modules.generated.sklearn.neighbors.radiusneighborstransformer#sklearn.neighbors.RadiusNeighborsTransformer.transform |
sklearn.neighbors.radius_neighbors_graph(X, radius, *, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False, n_jobs=None) [source]
Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Read more in the User G... | sklearn.modules.generated.sklearn.neighbors.radius_neighbors_graph#sklearn.neighbors.radius_neighbors_graph |
class sklearn.neural_network.BernoulliRBM(n_components=256, *, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None) [source]
Bernoulli Restricted Boltzmann Machine (RBM). A Restricted Boltzmann Machine with binary visible units and binary hidden units. Parameters are estimated using Stochastic M... | sklearn.modules.generated.sklearn.neural_network.bernoullirbm#sklearn.neural_network.BernoulliRBM |
sklearn.neural_network.BernoulliRBM
class sklearn.neural_network.BernoulliRBM(n_components=256, *, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None) [source]
Bernoulli Restricted Boltzmann Machine (RBM). A Restricted Boltzmann Machine with binary visible units and binary hidden units. Param... | sklearn.modules.generated.sklearn.neural_network.bernoullirbm |
fit(X, y=None) [source]
Fit the model to the data X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data. Returns
selfBernoulliRBM
The fitted model. | sklearn.modules.generated.sklearn.neural_network.bernoullirbm#sklearn.neural_network.BernoulliRBM.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.neural_network.bernoullirbm#sklearn.neural_network.BernoulliRBM.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.neural_network.bernoullirbm#sklearn.neural_network.BernoulliRBM.get_params |
gibbs(v) [source]
Perform one Gibbs sampling step. Parameters
vndarray of shape (n_samples, n_features)
Values of the visible layer to start from. Returns
v_newndarray of shape (n_samples, n_features)
Values of the visible layer after one Gibbs step. | sklearn.modules.generated.sklearn.neural_network.bernoullirbm#sklearn.neural_network.BernoulliRBM.gibbs |
partial_fit(X, y=None) [source]
Fit the model to the data X which should contain a partial segment of the data. Parameters
Xndarray of shape (n_samples, n_features)
Training data. Returns
selfBernoulliRBM
The fitted model. | sklearn.modules.generated.sklearn.neural_network.bernoullirbm#sklearn.neural_network.BernoulliRBM.partial_fit |
score_samples(X) [source]
Compute the pseudo-likelihood of X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Values of the visible layer. Must be all-boolean (not checked). Returns
pseudo_likelihoodndarray of shape (n_samples,)
Value of the pseudo-likelihood (proxy for likeliho... | sklearn.modules.generated.sklearn.neural_network.bernoullirbm#sklearn.neural_network.BernoulliRBM.score_samples |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.neural_network.bernoullirbm#sklearn.neural_network.BernoulliRBM.set_params |
transform(X) [source]
Compute the hidden layer activation probabilities, P(h=1|v=X). Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The data to be transformed. Returns
hndarray of shape (n_samples, n_components)
Latent representations of the data. | sklearn.modules.generated.sklearn.neural_network.bernoullirbm#sklearn.neural_network.BernoulliRBM.transform |
class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=100, activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_mo... | sklearn.modules.generated.sklearn.neural_network.mlpclassifier#sklearn.neural_network.MLPClassifier |
sklearn.neural_network.MLPClassifier
class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=100, activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_... | sklearn.modules.generated.sklearn.neural_network.mlpclassifier |
fit(X, y) [source]
Fit the model to data matrix X and target(s) y. Parameters
Xndarray or sparse matrix of shape (n_samples, n_features)
The input data.
yndarray of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression). Returns
self... | sklearn.modules.generated.sklearn.neural_network.mlpclassifier#sklearn.neural_network.MLPClassifier.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.neural_network.mlpclassifier#sklearn.neural_network.MLPClassifier.get_params |
property partial_fit
Update the model with a single iteration over the given data. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
yarray-like of shape (n_samples,)
The target values.
classesarray of shape (n_classes,), default=None
Classes across all calls to pa... | sklearn.modules.generated.sklearn.neural_network.mlpclassifier#sklearn.neural_network.MLPClassifier.partial_fit |
predict(X) [source]
Predict using the multi-layer perceptron classifier Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data. Returns
yndarray, shape (n_samples,) or (n_samples, n_classes)
The predicted classes. | sklearn.modules.generated.sklearn.neural_network.mlpclassifier#sklearn.neural_network.MLPClassifier.predict |
predict_log_proba(X) [source]
Return the log of probability estimates. Parameters
Xndarray of shape (n_samples, n_features)
The input data. Returns
log_y_probndarray of shape (n_samples, n_classes)
The predicted log-probability of the sample for each class in the model, where classes are ordered as they... | sklearn.modules.generated.sklearn.neural_network.mlpclassifier#sklearn.neural_network.MLPClassifier.predict_log_proba |
predict_proba(X) [source]
Probability estimates. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data. Returns
y_probndarray of shape (n_samples, n_classes)
The predicted probability of the sample for each class in the model, where classes are ordered as they are in se... | sklearn.modules.generated.sklearn.neural_network.mlpclassifier#sklearn.neural_network.MLPClassifier.predict_proba |
score(X, y, sample_weight=None) [source]
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_featur... | sklearn.modules.generated.sklearn.neural_network.mlpclassifier#sklearn.neural_network.MLPClassifier.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.neural_network.mlpclassifier#sklearn.neural_network.MLPClassifier.set_params |
class sklearn.neural_network.MLPRegressor(hidden_layer_sizes=100, activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_mom... | sklearn.modules.generated.sklearn.neural_network.mlpregressor#sklearn.neural_network.MLPRegressor |
sklearn.neural_network.MLPRegressor
class sklearn.neural_network.MLPRegressor(hidden_layer_sizes=100, activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_st... | sklearn.modules.generated.sklearn.neural_network.mlpregressor |
fit(X, y) [source]
Fit the model to data matrix X and target(s) y. Parameters
Xndarray or sparse matrix of shape (n_samples, n_features)
The input data.
yndarray of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression). Returns
self... | sklearn.modules.generated.sklearn.neural_network.mlpregressor#sklearn.neural_network.MLPRegressor.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.neural_network.mlpregressor#sklearn.neural_network.MLPRegressor.get_params |
property partial_fit
Update the model with a single iteration over the given data. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
yndarray of shape (n_samples,)
The target values. Returns
selfreturns a trained MLP model. | sklearn.modules.generated.sklearn.neural_network.mlpregressor#sklearn.neural_network.MLPRegressor.partial_fit |
predict(X) [source]
Predict using the multi-layer perceptron model. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data. Returns
yndarray of shape (n_samples, n_outputs)
The predicted values. | sklearn.modules.generated.sklearn.neural_network.mlpregressor#sklearn.neural_network.MLPRegressor.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.neural_network.mlpregressor#sklearn.neural_network.MLPRegressor.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.neural_network.mlpregressor#sklearn.neural_network.MLPRegressor.set_params |
class sklearn.pipeline.FeatureUnion(transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False) [source]
Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine sev... | sklearn.modules.generated.sklearn.pipeline.featureunion#sklearn.pipeline.FeatureUnion |
sklearn.pipeline.FeatureUnion
class sklearn.pipeline.FeatureUnion(transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False) [source]
Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the result... | sklearn.modules.generated.sklearn.pipeline.featureunion |
fit(X, y=None, **fit_params) [source]
Fit all transformers using X. Parameters
Xiterable or array-like, depending on transformers
Input data, used to fit transformers.
yarray-like of shape (n_samples, n_outputs), default=None
Targets for supervised learning. Returns
selfFeatureUnion
This estimator | sklearn.modules.generated.sklearn.pipeline.featureunion#sklearn.pipeline.FeatureUnion.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit all transformers, transform the data and concatenate results. Parameters
Xiterable or array-like, depending on transformers
Input data to be transformed.
yarray-like of shape (n_samples, n_outputs), default=None
Targets for supervised learning. Returns ... | sklearn.modules.generated.sklearn.pipeline.featureunion#sklearn.pipeline.FeatureUnion.fit_transform |
get_feature_names() [source]
Get feature names from all transformers. Returns
feature_nameslist of strings
Names of the features produced by transform. | sklearn.modules.generated.sklearn.pipeline.featureunion#sklearn.pipeline.FeatureUnion.get_feature_names |
get_params(deep=True) [source]
Get parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the transformer_list of the FeatureUnion. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects... | sklearn.modules.generated.sklearn.pipeline.featureunion#sklearn.pipeline.FeatureUnion.get_params |
set_params(**kwargs) [source]
Set the parameters of this estimator. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in tranformer_list. Returns
self | sklearn.modules.generated.sklearn.pipeline.featureunion#sklearn.pipeline.FeatureUnion.set_params |
transform(X) [source]
Transform X separately by each transformer, concatenate results. Parameters
Xiterable or array-like, depending on transformers
Input data to be transformed. Returns
X_tarray-like or sparse matrix of shape (n_samples, sum_n_components)
hstack of results of transformers. sum_n_compon... | sklearn.modules.generated.sklearn.pipeline.featureunion#sklearn.pipeline.FeatureUnion.transform |
sklearn.pipeline.make_pipeline(*steps, memory=None, verbose=False) [source]
Construct a Pipeline from the given estimators. This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically.... | sklearn.modules.generated.sklearn.pipeline.make_pipeline#sklearn.pipeline.make_pipeline |
sklearn.pipeline.make_union(*transformers, n_jobs=None, verbose=False) [source]
Construct a FeatureUnion from the given transformers. This is a shorthand for the FeatureUnion constructor; it does not require, and does not permit, naming the transformers. Instead, they will be given names automatically based on their ... | sklearn.modules.generated.sklearn.pipeline.make_union#sklearn.pipeline.make_union |
class sklearn.pipeline.Pipeline(steps, *, memory=None, verbose=False) [source]
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be ‘transforms’, that is, they must implement fit and transform methods. The final estima... | sklearn.modules.generated.sklearn.pipeline.pipeline#sklearn.pipeline.Pipeline |
sklearn.pipeline.Pipeline
class sklearn.pipeline.Pipeline(steps, *, memory=None, verbose=False) [source]
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be ‘transforms’, that is, they must implement fit and transfo... | sklearn.modules.generated.sklearn.pipeline.pipeline |
decision_function(X) [source]
Apply transforms, and decision_function of the final estimator Parameters
Xiterable
Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns
y_scorearray-like of shape (n_samples, n_classes) | sklearn.modules.generated.sklearn.pipeline.pipeline#sklearn.pipeline.Pipeline.decision_function |
fit(X, y=None, **fit_params) [source]
Fit the model Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator. Parameters
Xiterable
Training data. Must fulfill input requirements of first step of the pipeline.
yiterable, default=None
Training ... | sklearn.modules.generated.sklearn.pipeline.pipeline#sklearn.pipeline.Pipeline.fit |
fit_predict(X, y=None, **fit_params) [source]
Applies fit_predict of last step in pipeline after transforms. Applies fit_transforms of a pipeline to the data, followed by the fit_predict method of the final estimator in the pipeline. Valid only if the final estimator implements fit_predict. Parameters
Xiterable
... | sklearn.modules.generated.sklearn.pipeline.pipeline#sklearn.pipeline.Pipeline.fit_predict |
fit_transform(X, y=None, **fit_params) [source]
Fit the model and transform with the final estimator Fits all the transforms one after the other and transforms the data, then uses fit_transform on transformed data with the final estimator. Parameters
Xiterable
Training data. Must fulfill input requirements of f... | sklearn.modules.generated.sklearn.pipeline.pipeline#sklearn.pipeline.Pipeline.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the steps of the Pipeline. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estim... | sklearn.modules.generated.sklearn.pipeline.pipeline#sklearn.pipeline.Pipeline.get_params |
property inverse_transform
Apply inverse transformations in reverse order All estimators in the pipeline must support inverse_transform. Parameters
Xtarray-like of shape (n_samples, n_transformed_features)
Data samples, where n_samples is the number of samples and n_features is the number of features. Must fulf... | sklearn.modules.generated.sklearn.pipeline.pipeline#sklearn.pipeline.Pipeline.inverse_transform |
predict(X, **predict_params) [source]
Apply transforms to the data, and predict with the final estimator Parameters
Xiterable
Data to predict on. Must fulfill input requirements of first step of the pipeline.
**predict_paramsdict of string -> object
Parameters to the predict called at the end of all transfo... | sklearn.modules.generated.sklearn.pipeline.pipeline#sklearn.pipeline.Pipeline.predict |
predict_log_proba(X) [source]
Apply transforms, and predict_log_proba of the final estimator Parameters
Xiterable
Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns
y_scorearray-like of shape (n_samples, n_classes) | sklearn.modules.generated.sklearn.pipeline.pipeline#sklearn.pipeline.Pipeline.predict_log_proba |
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