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classes | Q_Score int64 0 1.72k | CreationDate stringlengths 23 23 | Users Score int64 -11 327 | AnswerCount int64 1 31 | System Administration and DevOps int64 0 1 | Title stringlengths 15 149 | Q_Id int64 5.14k 60M | Score float64 -1 1.2 | Tags stringlengths 6 90 | Answer stringlengths 18 5.54k | Question stringlengths 49 9.42k | Web Development int64 0 1 | Data Science and Machine Learning int64 1 1 | ViewCount int64 7 3.27M |
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0 | 40,284,356 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2016-10-27T12:12:00.000 | 4 | 1 | 0 | python: split matrix in hermitian and anti-hermitian part | 40,284,296 | 1.2 | python,numpy,matrix | The Hermitian part is (A + A.T.conj())/2, the anti-hermitian part is (A - A.T.conj())/2 (it is quite easy to prove).
If A = B + C with B Hermitian and C anti-Hermitian, you can take the conjugate (I'll denote it *) on both sides, uses its linearity and obtain A* = B - C, from which the values of B and C follow easily. | Imagine I have a numpy array in python that has complex numbers as its elements.
I would like to know if it is possible to split any matrix of this kind into a hermitian and anti-hermitian part? My intuition says that this is possible, similar to the fact that any function can be split into an even and an uneven part.
... | 0 | 1 | 309 |
0 | 40,293,068 | 0 | 0 | 0 | 0 | 1 | false | 4 | 2016-10-27T14:20:00.000 | 4 | 1 | 0 | Fastest way to calculate many regressions in python? | 40,287,113 | 0.664037 | python,python-3.x,numpy,linear-regression | some brief answers
1) Calling statsmodels repeatedly is not the fastest way. If we just need parameters, prediction and residual and we have identical explanatory variables, then I usually just use params = pinv(x).dot(y) where y is 2 dimensional and calculate the rest from there. The problem is that inference, confide... | I think I have a pretty reasonable idea on how to do go about accomplishing this, but I'm not 100% sure on all of the steps. This question is mostly intended as a sanity check to ensure that I'm doing this in the most efficient way, and that my math is actually sound (since my statistics knowledge is not completely per... | 0 | 1 | 3,105 |
0 | 40,290,127 | 0 | 1 | 0 | 0 | 1 | false | 5 | 2016-10-27T16:35:00.000 | 1 | 5 | 0 | Converting a 3D List to a 3D NumPy array | 40,289,943 | 0.039979 | python,list,python-3.x,numpy | Looping and adding is likely better, since you want to preserve the structure of the original. Plus, the error you mentioned indicates that you would need to flatten the numpy array and then add to each element. Although numpy operations tend to be faster than list operations, converting, flattening, and reverting is c... | Currently, I have a 3D Python list in jagged array format.
A = [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0], [0], [0]]]
Is there any way I could convert this list to a NumPy array, in order to use certain NumPy array operators such as adding a number to each element.
A + 4 would give [[[4, 4, 4], [4, 4, 4], [4, 4, 4]], [[4... | 0 | 1 | 7,011 |
0 | 41,733,461 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-10-28T01:49:00.000 | 1 | 1 | 0 | How to extract words used for Doc2Vec | 40,296,765 | 0.197375 | python,nlp,gensim,doc2vec | Gensim's Word2Vec/Doc2Vec models don't store the corpus data – they only examine it, in multiple passes, to train up the model. If you need to retrieve the original texts, you should populate your own lookup-by-key data structure, such as a Python dict (if all your examples fit in memory).
Separately, in recent versio... | I am preparing a Doc2Vec model using tweets. Each tweet's word array is considered as a separate document and is labeled as "SENT_1", SENT_2" etc.
taggeddocs = []
for index,i in enumerate(cleaned_tweets):
if len(i) > 2: # Non empty tweets
sentence = TaggedDocument(words=gensim.utils.to_unicode(i).split(), ... | 0 | 1 | 1,260 |
0 | 40,315,040 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-10-28T06:04:00.000 | 2 | 2 | 0 | How to handle nominal data in scikit learn, python? | 40,298,966 | 1.2 | python,scikit-learn,data-mining,categorical-data | Depends what type of model you're using, make_pipeline(LabelEncoder, OneHotEncoder) or pd.get_dummies) is the usual choice, and can work well with classifiers from either linear_model or tree. LabelEncoder on its own would another choice, although this won't work well unless there's a natural ordering on your labels (l... | I am new to data mining. I have a data set which includes directors' names. What is the right way to convert them to something that Scikit learn estimators can use without problem?
From what I found on the internet I thought that sklearn.preprocessing.LabelEncoder is the right choice. | 0 | 1 | 861 |
0 | 40,299,648 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-10-28T06:46:00.000 | 0 | 2 | 0 | Python groupby threshold | 40,299,538 | 0 | python,group-by,itertools | Bucket them. You'd need to manually work out the breaks in advance. Can you sort then, in advance? That would make it easier.
Actually, if you use log, then a multiplicative threshold turns into a constant threshold, e.g. 0.98..1.02 in log-land ~= (-0.02, +0.02).
So, use the log of all your numbers.
You'll still need t... | I have a list of numbers and I need to group it. itertools.grouby work perfectly for sequences of same numbers but I need same behavior for numbers with a threshold (2-3%)
E.X: lst = [1, 500, 19885, 19886, 19895, 90000000]
and I expect [[1], [500], [19885, 19886, 19895], [90000000]]
Can you suggest me something? | 0 | 1 | 415 |
0 | 40,310,470 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2016-10-28T16:55:00.000 | 2 | 2 | 0 | cannot sum rows that match a regular expression in pandas / python | 40,309,777 | 0.197375 | python,regex,pandas | The problem is that the match function does not return True when it matches, it returns a match object. Pandas cannot add this match object because it is not an integer value. The reason you get a sum when you are using 'not' is because it returns a boolean value of True, which pandas can sum the True value and return ... | I can find the number of rows in a column in a pandas dataframe that do NOT follow a pattern but not the number of rows that follow the very same pattern!
This works:
df.report_date.apply(lambda x: (not re.match(r'[0-9]{4}-[0-9]{1,2}-[0-9]{1,2}', x))).sum()
This does not: removing 'not' does not tell me how many rows m... | 0 | 1 | 883 |
0 | 40,351,277 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-10-30T05:05:00.000 | 0 | 1 | 0 | How to do matmul operation in specific dimension in tensorflow | 40,326,169 | 0 | python,tensorflow,deep-learning | I would first reshape your Tensor to be (sequence_length * batch_size, word_dim), do the matmul to get (sequence_length * batch_size, hidden_dim), then reshape again to get (sequence_length, batch_size, hidden_dim). There is no copying involved with reshape(), and this is equivalent to multiplying each of the batch_siz... | I have a 3D tensor (sequence_length, batch_size, word_dim), I need to do matmul operation with "word_dim" dimension so that I can change tensor into (sequence_length, batch_size, hidden_dim). It seems that matmul operation can only be used in 2D tensor. And I can not change the 3D tensor into 2D because of the "batch_s... | 0 | 1 | 118 |
0 | 52,020,891 | 0 | 0 | 0 | 0 | 1 | false | 4 | 2016-10-30T12:52:00.000 | 0 | 2 | 0 | Multiscale CNN - Keras Implementation | 40,329,307 | 0 | python,neural-network,concatenation,convolution,keras | I do not understand why to have 3 CNNs because you would mostly have the same results than on a single CNN. Maybe you could train faster.
Perhaps you could also do pooling and some resnet operation (I guess this could prove similar to what you want).
Nevertheless, for each CNN you need a cost function in order to optim... | I want to implement a multiscale CNN in python. My aim is to use three different CNNs for three different scales and concatenate the final outputs of the final layers and feed them to a FC layer to take the output predictions.
But I don't understand how can I implement this. I know how to implement a single scale CNN. ... | 0 | 1 | 1,527 |
0 | 40,340,683 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-10-31T09:20:00.000 | 1 | 1 | 0 | Plot maximum of whats remaining on plot after xlim | 40,339,355 | 1.2 | python,matplotlib,contour | Without providing any code it's hard to give you a code example of what you should do, but I'm assuming you are providing contourplot some 2d numpy array of values that drives the visualization. What I would suggest is then to set the x-limit in that data-structure rather than providing the limit to matplotlib. If Xis ... | I am plotting a contour on matplotlib using the contourf command.
I get huge numbers at the start of frequency, but lower peaks after that.
xlim only works in hiding the higher numbers - but I want the lower peaks to become the maximum on the colorbar (not shown in my images).
How to rescale the contour after the xlim ... | 0 | 1 | 30 |
0 | 40,349,994 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-10-31T20:12:00.000 | 0 | 1 | 0 | How to convert quantlib.time.date.Date to pandas.tslib.Timestamp | 40,349,868 | 0 | python,pandas,quantlib | Looks like something like this might work:
dataframe.set_index = [ pandas.to_datetime(str(a)) for a in list(data.keys()) ]
but I get the following error:
KeyError: Timestamp('2016-09-27 00:00:00')
Press any key to continue . . . | I have a list data.keys() which returns
dict_keys([2016-09-27, 2016-09-28, 2016-09-29, 2016-09-30, 2016-09-26])
Data type for each of the element is quantlib.time.date.Date
How do I create an index for pandas with the above so that the index is of type pandas.tslib.Timestamp? | 0 | 1 | 411 |
0 | 40,370,495 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-11-01T21:35:00.000 | 1 | 1 | 0 | Python MLPClassifier Value Error | 40,369,042 | 1.2 | python,machine-learning,scikit-learn,artificial-intelligence | Your data does not make any sense from scikit-learn perspective of what is expected in the .fit call. Feature vectors is supposed to be a matrix of size N x d, where N - number of data points and d number of features, and your second variable should hold labels, thus it should be vector of length N (or N x k where k is... | I'm currently trying to train the MLPClassifier implemented in sklearn...
When i try to train it with the given values i get this error:
ValueError: setting an array element with a sequence.
The format of the feature_vector is
[ [one_hot_encoded brandname], [different apps scaled to mean 0 and variance 1] ]
Does anybo... | 0 | 1 | 437 |
0 | 40,370,096 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-01T23:07:00.000 | 0 | 1 | 0 | Generate new tensorflow tensor according to the element index of original tensor | 40,370,004 | 0 | python-2.7,tensorflow | You should just make your label (y) in your reduced sum format (i.e. 3 bits), and train to that label. The neural net should be smart enough to adjust the weights to imitate your reduce_sum logic. | I have a question about tensorflow tensor.
If I have a NeuralNet like y=xw+b as an example.
then x is placeholder([7,7] dims), w is Variable([7,1]) and b is Variable([1,1])
So, y is tensorflow tensor with [7,1] dims.
then, in this case. can I make a new tensor like
new_y = [tf.reduce_sum(y[0:3]), tf.reduce_sum(y[3:5]),... | 0 | 1 | 54 |
0 | 40,390,555 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-02T20:39:00.000 | -1 | 2 | 0 | Clustering before regression - recommender system | 40,389,402 | -0.099668 | python,machine-learning,scikit-learn,k-means | Clustering users makes sense. But if your only feature is the rating, I don't think it could produce a useful model for prediction. Below are my assumptions to make this justification:
The quality of movie should be distributed with a gaussion distribution.
If we look at the rating distribution of a common user, it sh... | I have a file called train.dat which has three fields - userID, movieID and rating.
I need to predict the rating in the test.dat file based on this.
I want to know how I can use scikit-learn's KMeans to group similar users given that I have only feature - rating.
Does this even make sense to do? After the clustering ... | 0 | 1 | 733 |
0 | 40,411,804 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2016-11-03T10:21:00.000 | 0 | 2 | 0 | python : facing memory issue in document clustering using sklearn | 40,399,023 | 0 | python,scikit-learn,cluster-analysis,tf-idf | start small.
First cluster only 100.00 documents. Only once it works (because it probably won't), then think about scaling up.
If you don't succeed clustering the subset (and text clusters are usually pretty bad), then you won't fare well on the large set. | I am using TfIdfVectorizer of sklearn for document clustering. I have 20 million texts, for which i want to compute clusters. But calculating TfIdf matrix is taking too much time and system is getting stuck.
Is there any technique to deal with this problem ? is there any alternative method for this in any python modul... | 0 | 1 | 261 |
0 | 40,399,357 | 0 | 0 | 0 | 0 | 2 | true | 0 | 2016-11-03T10:21:00.000 | 1 | 2 | 0 | python : facing memory issue in document clustering using sklearn | 40,399,023 | 1.2 | python,scikit-learn,cluster-analysis,tf-idf | Well, a corpus of 20 million texts is very large, and without a meticulous and comprehensive preprocessing nor some good computing instances (i.e. a lot of memory and good CPUs), the TF-IDF calculation may take a lot of time.
What you can do :
Limit your text corpus to some hundred of thousands of samples (let's say ... | I am using TfIdfVectorizer of sklearn for document clustering. I have 20 million texts, for which i want to compute clusters. But calculating TfIdf matrix is taking too much time and system is getting stuck.
Is there any technique to deal with this problem ? is there any alternative method for this in any python modul... | 0 | 1 | 261 |
0 | 40,413,120 | 1 | 0 | 0 | 0 | 1 | true | 3 | 2016-11-03T20:48:00.000 | 3 | 1 | 0 | Does EMR still have any advantages over EC2 for Spark? | 40,410,975 | 1.2 | python-3.x,apache-spark,amazon-ec2 | This question boils down to the value of managed services, IMHO.
Running Spark as a standalone in local mode only requires you get the latest Spark, untar it, cd to its bin path and then running spark-submit, etc
However, creating a multi-node cluster that runs in cluster mode requires that you actually do real network... | I know this question has been asked before but those answers seem to revolve around Hadoop. For Spark you don't really need all the extra Hadoop cruft. With the spark-ec2 script (available via GitHub for 2.0) your environment is prepared for Spark. Are there any compelling use cases (other than a far superior boto3 ... | 0 | 1 | 129 |
0 | 40,643,572 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2016-11-03T21:14:00.000 | 0 | 2 | 0 | How to classify imbalanced data in weka? | 40,411,357 | 0 | python,weka | Not sure about python but in the gui version you can use SpreadSubsample to reduce the class imbalance. If you feel that 'bad' is a a good representation of the class then you could experiment with different number of instances of 'good.'
To do this you need to select Filter ==> Supervised ==> Instance ==> SpreadSubsam... | I have an imbalanced training data and i am using logistic regression in weka to classify.
There are two classes good and bad. Good has 75000 instances and bad
3000. My test data has 10000 good data.
When i train it is more inclined to good data i.e it classifies almost all bad instances good. What should i do ?
I... | 0 | 1 | 1,258 |
0 | 40,837,930 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2016-11-03T21:14:00.000 | 0 | 2 | 0 | How to classify imbalanced data in weka? | 40,411,357 | 0 | python,weka | There are a couple of things that you could try.
Use Boosting (AdaBoostM1) so that the misclassified instances will be given extra weight.
Use weka.classifiers.meta.CostSensitiveClassifier and give the "bad" instances a higher weight than the "good" instances. Note: This will probably reduce your overall accurac... | I have an imbalanced training data and i am using logistic regression in weka to classify.
There are two classes good and bad. Good has 75000 instances and bad
3000. My test data has 10000 good data.
When i train it is more inclined to good data i.e it classifies almost all bad instances good. What should i do ?
I... | 0 | 1 | 1,258 |
0 | 40,411,415 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-11-03T21:15:00.000 | 0 | 1 | 0 | Using numpy.nanargmin() in 2 dimensional matrix | 40,411,374 | 1.2 | python,python-3.x,numpy | NumPy arrays only have the argmin() attribute, but no nanargmin() attribute. So A.nanargmin() does not exist.
You can use numpy.argmin(A) and numpy.nanargmin(A) instead. | I am trying to obtain the argmin of a numpy 2 dimensional array A which has nan values. Now the problem is:
numpy.nanargmin(A) returns only one index.
numpy.unravel_index(A.argmin(), A.shape) returns [0,0] because it has nan values.
And...
numpy.unravel_index(A.nanargmin(), A.shape) throws the error:
AttributeError... | 0 | 1 | 471 |
0 | 40,433,924 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-05T01:26:00.000 | 2 | 1 | 0 | Numpy fails to serve as a dependency for pandas | 40,433,906 | 0.379949 | python,pandas,numpy | I would recommend not building from source on Windows unless you really know what you're doing.
Also, don't mix conda and pip for numpy; numpy is treated specially in conda and really should work out of the box. If you get an error on import pandas there's likely something wrong with your PATH or PYTHONPATH.
I suggest ... | I was trying to use pandas (installed the binaries and dependencies using conda, then using pip, then built then using no-binaries option); still getting error.
Numpy is available (1.11.2).
I understand some interface is not provided by numpy anymore.
Python version I am using is 2.7.11.
List of packages installed are ... | 0 | 1 | 1,990 |
0 | 40,446,697 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2016-11-06T06:30:00.000 | 0 | 1 | 0 | Why is it dataframe.head() in python and head(dataframe) in R? Why is python like this in general? | 40,446,650 | 1.2 | python | It's simple:
foo.bar() does the same thing as foo.__class__.bar(foo)
so it is a function, and the argument is passed to it, but the function is stored attached to the object via its class (type), so to say. The foo.bar() notation is just shorthand for the above.
The advantage is that different functions of the sams nam... | Beginner here. Shouldnt the required variables be passed as arguments to the function. Why is it variable.function() in python? | 0 | 1 | 139 |
0 | 40,603,239 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2016-11-07T03:20:00.000 | 1 | 2 | 0 | information retrieval evaluation python precision, recall, f score, AP,MAP | 40,457,331 | 0.099668 | python,information-retrieval,information-extraction | Evaluation has two essentials. First one is a test resource with the ranking of documents or their relevancy tag (relevant or not-relevant) for specific queries, which is made with an experiment (like user click, etc. and is mostly used when you have a running IR system), or made through crowd-sourcing. The second esse... | i wrote one program to do the information retrieval and extraction. user enter the query in the search bar, the program can show the relevant txt result such as the relevant sentence and the article which consists the sentence.
I did some research for how to evaluate the result. I might need to calculate the precision,... | 0 | 1 | 6,407 |
0 | 40,473,264 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-11-07T12:22:00.000 | 1 | 1 | 0 | Python & scikit Learn: replace matrix vector product during training with custom call | 40,465,084 | 1.2 | python,matrix,vector,machine-learning,scikit-learn | In short - no, it is not possible. Mostly because some aritchmetical operations are not even performed in python when you use scikit-learn - they are actually performed by C-based extensions (like libsvm library). You could monkey patch .dot of numpy to do what you want, but you do not have any guarantee that scikit-le... | I've looked through the documentation over at scikit learn and I haven't seen a straightforward way to replace the matrix-vector product evaluations during propagation with a custom evaluation call.
Is there a way to do this that's already part of the API...or are there any tricks that would allow me to inject a custom... | 0 | 1 | 38 |
0 | 40,473,685 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-11-07T18:10:00.000 | 2 | 1 | 0 | Finding minimum indepedent dominating set using a greedy algorithm | 40,471,747 | 0.379949 | python,algorithm,graph-theory,networkx,independent-set | Unfortunately, the problem of finding the minimum independent dominating set is NP-complete. Hence, any known algorithm which is sound and complete will be inefficient.
A possible approach is to use an incomplete algorithm (aka local search).
For example, the following algorithm is known to have a factor (1 + log|V|) a... | I developed an algorithm that finds the minimum independent dominating set of a graph based on a distance constraint. (I used Python and NetworkX to generate graphs and get the pairs)
The algorithm uses a brute force approach:
Find all possible pairs of edges
Check which nodes satisfy the distance constraint
Find all ... | 0 | 1 | 1,579 |
1 | 40,672,019 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-11-07T19:01:00.000 | 0 | 3 | 0 | Tensorflow on android: directly build app in python? | 40,472,585 | 0 | android,python,tensorflow | I tried to use python in my android application with some 3rd party terminals like SL4A and Qpython. Those will support to run the python files directly in our android application so we have to install SL4A apk's and we need to call that intent.But these will support for some level I guess.
I tried to import tensorflow... | The sample app given by google for tensorflow on android is written in C++.
I have a tensorflow application written in python. This application currently runs on desktop. I want to move the application to android platform. Can I use bazel to build the application that is written in python directly for android? Thanks.
... | 0 | 1 | 534 |
0 | 40,523,772 | 0 | 0 | 0 | 0 | 1 | true | 12 | 2016-11-08T11:06:00.000 | 4 | 4 | 0 | Retrieve list of training features names from classifier | 40,485,285 | 1.2 | python,pandas,scikit-learn,random-forest | Based on the documentation and previous experience, there is no way to get a list of the features considered at least at one of the splitting.
Is your concern that you do not want to use all your features for prediction, just the ones actually used for training? In this case I suggest to list the feature_importances_ a... | Is there a way to retrieve the list of feature names used for training of a classifier, once it has been trained with the fit method? I would like to get this information before applying to unseen data.
The data used for training is a pandas DataFrame and in my case, the classifier is a RandomForestClassifier. | 0 | 1 | 17,091 |
0 | 43,329,072 | 0 | 1 | 0 | 0 | 2 | false | 9 | 2016-11-08T15:57:00.000 | 5 | 3 | 0 | How to calculate 95% confidence intervals using Bootstrap method | 40,491,298 | 0.321513 | python,statistics | I have a simple statistical solution :
Confidence intervals are based on the standard error.
The standard error in your case is the standard deviation of your 1000 bootstrap means. Assuming a normal distribution of the sampling distribution of your parameter(mean), which should be warranted by the properties of the C... | I'm trying to calculate the confidence interval for the mean value using the method of bootstrap in python. Let say I have a vector a with 100 entries and my aim is to calculate the mean value of these 100 values and its 95% confidence interval using bootstrap. So far I have manage to resample 1000 times from my vector... | 0 | 1 | 15,818 |
0 | 40,491,405 | 0 | 1 | 0 | 0 | 2 | true | 9 | 2016-11-08T15:57:00.000 | 8 | 3 | 0 | How to calculate 95% confidence intervals using Bootstrap method | 40,491,298 | 1.2 | python,statistics | You could sort the array of 1000 means and use the 50th and 950th elements as the 90% bootstrap confidence interval.
Your set of 1000 means is basically a sample of the distribution of the mean estimator (the sampling distribution of the mean). So, any operation you could do on a sample from a distribution you can do h... | I'm trying to calculate the confidence interval for the mean value using the method of bootstrap in python. Let say I have a vector a with 100 entries and my aim is to calculate the mean value of these 100 values and its 95% confidence interval using bootstrap. So far I have manage to resample 1000 times from my vector... | 0 | 1 | 15,818 |
0 | 40,492,219 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2016-11-08T16:17:00.000 | 5 | 1 | 0 | Get cluster members/elements clustering with scikit-learn DBSCAN | 40,491,707 | 1.2 | python,machine-learning,scikit-learn | I believe you are asking for the cluster assignment of each item in your dataset, X.
You can use the labels_ attribute. db.labels_ Each index here corresponds to the same index in X, so you can see the assignments. | I use dbscan scikit-learn algorithm for clustering.
db = DBSCAN().fit(X) returns me 8 for example. My goal is to recover the cluster by cluster components. I said that X is a vector to vector and what I expect when I speak of cluster members, it is the sub-vectors of X.
Is there anyone to help me? | 0 | 1 | 3,915 |
0 | 40,543,983 | 0 | 1 | 0 | 0 | 1 | false | 4 | 2016-11-09T16:40:00.000 | 3 | 3 | 0 | Using pyfmi with multiprocessing for simulation of Modelica FMUs | 40,511,920 | 0.197375 | python-2.7,modelica,fmi | The problem is that pyfmi.fmiFMUModelCS2 is a Cython class dependent on external libraries which makes it unpickable. So it is not possible unfortunately.
If you want to use multiprocessing the only way forward that I see is that you first create the processes and then load the FMUs into the separate processes. In this... | I am trying to simulate multiple Modelica FMUs in parallel using python/pyfmi and multiprocessing. However I am not able to return any pyfmi FMI objects from the subprocesses once the FMUs are initialized. It seems that pyfmi FMI objects (e.g. pyfmi.fmi.FMUModelCS2 or pyfmi.fmi.FMUState2) are not pickable. I also tried... | 0 | 1 | 1,150 |
0 | 40,789,964 | 0 | 0 | 0 | 0 | 2 | false | 3 | 2016-11-09T18:15:00.000 | 0 | 3 | 0 | tensorflow retrain.py app.run() got unexpected keyword argument 'argv' | 40,513,466 | 0 | python,tensorflow | Please check your sample's version. I met the same problem and finally solved it. I found my tf version is 0.11, but I downloaded the master one,
then I compare the code asyntax difference. | I am trying to run the Tensorflow for Poets sample. I pass the following:
python examples/image_retraining/retrain.py --bottlenext_dir=tf_files/bottlenecks --how_many_training_steps 500 --model_dir=tf_files/inception --output_graph=tf_files/retrained_graph.pb --output_labels=tf_files/retrained_labels.txt --image_dir t... | 0 | 1 | 3,375 |
0 | 40,622,206 | 0 | 0 | 0 | 0 | 2 | false | 3 | 2016-11-09T18:15:00.000 | -1 | 3 | 0 | tensorflow retrain.py app.run() got unexpected keyword argument 'argv' | 40,513,466 | -0.066568 | python,tensorflow | You can also specifically checkout just the working fully_connected_feed.py file from the r0.11 branch by using the git command:
git checkout 5b18edb fully_connected_feed.py
NOTE: You need to be in the mnist/ directory to use this command | I am trying to run the Tensorflow for Poets sample. I pass the following:
python examples/image_retraining/retrain.py --bottlenext_dir=tf_files/bottlenecks --how_many_training_steps 500 --model_dir=tf_files/inception --output_graph=tf_files/retrained_graph.pb --output_labels=tf_files/retrained_labels.txt --image_dir t... | 0 | 1 | 3,375 |
0 | 40,522,400 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-10T07:34:00.000 | 0 | 1 | 0 | Scipy hierarchial clustering - clusterize new vector | 40,522,186 | 0 | python,numpy,vector,scipy | Use the euclidean metric? I think the precomputed clusters have a center. So you can calculate the distanc between every center and the new vector. | I am clusterizing a bunch of feature vectors using scipy linkage with ward method.
I want a predictive model that works in two steps:
Training data is clusterized
A new vector comes, the distance between the vector and each cluster is computed, the new vector is assigned the "nearest" cluster's label
How can I compu... | 0 | 1 | 27 |
0 | 40,527,574 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-11-10T12:21:00.000 | 0 | 1 | 0 | installing numpy on a python 2.7 with system windows 8.1 | 40,527,528 | 0 | python-2.7 | If you have pip installed on your system, execute
pip install numpy | I'm working on a code to run it in abaqus. I need in my code to use numpy module. I have python 2.7.11 on my computer. I have installed it on windows 8.1.
I have downloaded numpy-1.11.Zip already.
I look for an easy detailed guide for installing it on my python
Thank You! | 0 | 1 | 45 |
0 | 40,534,978 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-10T17:54:00.000 | 1 | 1 | 0 | Can I apply calculated gradient in tensorflow? | 40,534,098 | 0.197375 | python,machine-learning,tensorflow,deep-learning | When you create your network and attach some loss you call minimize on the optimizer, which (under the hood) calls "apply_gradients". This function adds gradient computing ops to your graph. All you have to do is now request the op responsible for your partial derivative and pass the precomputed partial derivative thro... | What I want to do is to simulate the back-propagation process on different machines, from one machine, I get the gradient from layer3 d(layer3_output)/d(layer2_output) as a numpy array, how am I able to get d(layer3_output)/d(layer1_output) efficiently given the gradient I received and passed to the previous layer? | 0 | 1 | 351 |
0 | 40,537,920 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-11-10T18:45:00.000 | 1 | 1 | 0 | Varying n_neighbors in scikit-learn KNN regression | 40,534,871 | 1.2 | python,scikit-learn,regression,knn | I'm afraid not. In part, this is due to some algebraic assumptions that the relationship is symmetric: A is a neighbour to B iff B is a neighbour to A. If you give different k values, you're guaranteed to break that symmetry.
I think the major reason is simply that the algorithm is simpler with a fixed quantity of ne... | I am using scikit-learn's KNN regressor to fit a model to a large dataset with n_neighbors = 100-500. Given the nature of the data, some parts (think: sharp delta-function like peaks) are better fit with fewer neighbors (n_neighbors ~ 20-50) so that the peaks are not smoothed out. The location of these peaks are known ... | 0 | 1 | 198 |
0 | 43,399,098 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-11-11T16:41:00.000 | 0 | 1 | 0 | Python - Importing pandas in console works but not when running script | 40,552,453 | 0 | python,email,pandas,import | I also had the same problem today. You're missing a specific path. Found that if you start your python interpreter and do import os, you can do os.environ. You'll notice that there are several paths set in the PATH variable. Copy/paste the entire PATH line into your script. That worked for me. Also, remember to remove ... | I've been working with anaconda3/python3.5 and pandas for over a year and all of sudden when I run my script outside the console, I get a import error for pandas particularly the dependency email.parser. I get No module named 'email.parser';'email' is not a package. However importing in the console works fine. I'm not ... | 0 | 1 | 831 |
0 | 40,555,741 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-11T17:10:00.000 | 0 | 1 | 0 | Input one fixed cluster centroid, find N others (python) | 40,552,937 | 0 | python-2.7,scikit-learn,cluster-analysis | Rather than recycling clustering for this, treat it as a regular optimization problem. You don't want to "discover structure", but optimize cost.
Beware that earth is not flat, and Euclidean distance (i.e. k-means) is a bad idea. 1 degree north is only at the equator approximately the same distance to 1 degree east. If... | I have a table of shipment destinations in lat, long. I have one fixed origination point (also lat, long). I would like to find other optimal origin locations using clustering. In other words, I want to assign one cluster centroid (keep it fixed) and find 1, 2, 3 . . . N other cluster centroids. Is this possible with t... | 0 | 1 | 512 |
0 | 62,375,979 | 0 | 0 | 0 | 0 | 1 | false | 11 | 2016-11-16T11:03:00.000 | -2 | 3 | 0 | Can we see the group data in pandas.core.groupby.SeriesGroupBy object | 40,630,506 | -0.132549 | python,pandas | Just adding list() should help but the structure isn't super useful in its raw form. It returns a list of tuples with index. | Can we check the data in a pandas.core.groupby.SeriesGroupBy object? | 0 | 1 | 12,663 |
0 | 40,676,992 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-11-16T12:23:00.000 | 0 | 1 | 0 | Store a csv file created by a python script on Hudson | 40,632,110 | 1.2 | python,csv,jenkins,hudson,store | I ran the windows command line to execute the python script and it worked. | I have a python script that creates a csv file and I have a Hudson job that builds every day. I want to store the csv file as an artifact but when I start the job nothing is stored or created. The build is successful and all tests are done but no file is created.
Do you know why this happens? | 0 | 1 | 42 |
0 | 40,635,363 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-16T13:28:00.000 | 0 | 1 | 0 | why python cv2.drawContours() just draw the first contours? | 40,633,399 | 0 | python-2.7 | Finally, I found what was the problem. I was passing the same contour to every step ([l1[0][2]]), so I need to change to operate with index, like [l1[i][2]]).
Sorry for that mistake and for your patience.
Have a good day, Jaime | I am having trouble filling a mask using cv2.drawContours, I dont know what could it be, or how to fix it.
I have a list with three elements's sublists [coordX,coordY, contour], so for every pair of coordinates, there is an if/else decision, each one with this stament: cv2.drawContours(mask,[l1[0][2]],0,c1[cla],-1)
[l1... | 0 | 1 | 92 |
0 | 63,427,934 | 0 | 1 | 0 | 0 | 2 | false | 3 | 2016-11-16T16:51:00.000 | 0 | 3 | 0 | import numpy not working in IDLE | 40,637,774 | 0 | python,numpy | Just use python -m before pip command, so that you won't get any problem while doing so in IDLE..
ex. python -m pip install scipy/numpy | I have python3.5.2 installed on a windows10 machine(Adding into the pythonpath is included in the setup with new python). I ,then, installed the Anaconda(4.2.0) version. At the command prompt when i run the python interpreter and import numpy it works fine. But when i save it as a script and try running from the IDLE, ... | 0 | 1 | 6,809 |
0 | 40,638,108 | 0 | 1 | 0 | 0 | 2 | false | 3 | 2016-11-16T16:51:00.000 | 2 | 3 | 0 | import numpy not working in IDLE | 40,637,774 | 0.132549 | python,numpy | You do have two versions of python installed: the CPython 3.5.2 distribution you mention first, and the Anaconda 4.2.0 Python distribution you then mention. Anaconda packages a large number of 3rd party packages, including Numpy. However, the CPython 3.5.2 installation available on python.org only ships with the standa... | I have python3.5.2 installed on a windows10 machine(Adding into the pythonpath is included in the setup with new python). I ,then, installed the Anaconda(4.2.0) version. At the command prompt when i run the python interpreter and import numpy it works fine. But when i save it as a script and try running from the IDLE, ... | 0 | 1 | 6,809 |
0 | 40,642,637 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-11-16T20:56:00.000 | 1 | 1 | 0 | Ensembles in Python | 40,642,132 | 1.2 | python,machine-learning,statistics,cross-validation,data-science | You have different ways to create an ensemble using your weak classifiers:
-Bagging: You can average the output of the 4 classifiers.
-Stacking: Your final output could be a linear combination of the 4 individual outputs. You can use the output of your 4 models as the input of a another algorithm or you can directly us... | I have implemented 4 classifiers using scikit-learn in Python. But, the performance on all of them is not very good. I want to implement an ensemble of these classifiers. I looked up the ensembles on scikit-learn but it has Random Forests and Adaboost. How should I create an ensemble of my weak classifiers? | 0 | 1 | 513 |
0 | 41,656,919 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-17T02:22:00.000 | 0 | 1 | 0 | Custom roll fix effects in regressions | 40,645,689 | 0 | python,r,statistics,regression | how plm and xtreg,fedo behind the screen are well documented in their instructions, I didn't read them through but I think that when you run pgmm or some thing like logit regression in plm,the algorithm actually is maximum likelihood given the nature of these problems.
and it would be interesting to write all these b... | In a regressions, I know instead of using fixed effects (say on country or year), you can specify these effects with dummy variables and this is a perfectly legitimate method. However, when the number of effects (e.g. countries) is large, then it becomes very computational difficult. Instead you would run the fixed eff... | 0 | 1 | 63 |
1 | 40,665,287 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-11-17T20:55:00.000 | 1 | 1 | 0 | Python and openCV to android APK | 40,664,845 | 0.197375 | android,python,opencv,apk | There is SL4A / PythonForAndroid but unfortunately it uses hardcoded Java RMI invocations for anything os related. That means that there are no OpenCV bindings.
I guess you'll have to learn Java | I want to transform my python code into an Android app. The problem I saw is I'm using openCV in the code, and I didn't found anything related to generate an APK while using openCV.
There is a way to generate an APK from python and openCV? | 0 | 1 | 292 |
0 | 40,670,426 | 0 | 0 | 0 | 0 | 2 | false | 28 | 2016-11-18T06:05:00.000 | 2 | 9 | 0 | Dot product of two vectors in tensorflow | 40,670,370 | 0.044415 | python,tensorflow,dot-product | You can do tf.mul(x,y), followed by tf.reduce_sum() | I was wondering if there is an easy way to calculate the dot product of two vectors (i.e. 1-d tensors) and return a scalar value in tensorflow.
Given two vectors X=(x1,...,xn) and Y=(y1,...,yn), the dot product is
dot(X,Y) = x1 * y1 + ... + xn * yn
I know that it is possible to achieve this by first broadcasting the v... | 0 | 1 | 64,462 |
0 | 40,672,159 | 0 | 0 | 0 | 0 | 2 | false | 28 | 2016-11-18T06:05:00.000 | 26 | 9 | 0 | Dot product of two vectors in tensorflow | 40,670,370 | 1 | python,tensorflow,dot-product | In addition to tf.reduce_sum(tf.multiply(x, y)), you can also do tf.matmul(x, tf.reshape(y, [-1, 1])). | I was wondering if there is an easy way to calculate the dot product of two vectors (i.e. 1-d tensors) and return a scalar value in tensorflow.
Given two vectors X=(x1,...,xn) and Y=(y1,...,yn), the dot product is
dot(X,Y) = x1 * y1 + ... + xn * yn
I know that it is possible to achieve this by first broadcasting the v... | 0 | 1 | 64,462 |
0 | 40,671,334 | 0 | 1 | 0 | 0 | 1 | true | 1 | 2016-11-18T07:00:00.000 | 5 | 2 | 0 | Use APIs for sorting or algorithm? | 40,671,158 | 1.2 | python,algorithm,api,data-structures | Why to use public APIs:
The built in methods were written and reviewed by very experienced and many coders, and a lot of effort was invested to optimize them to be as efficient as it gets.
Since the built in methods are public APIs, it is also means they are constantly used, which means you get a massive "free" testin... | In a programming language like Python Which will have better efficiency? if i use a sorting algorithm like merge sort to sort an array or If I use a built in API like sort() to sort the array? If Algorithms are independent of programming languages, then what is the advantage of algorithms over built in methods or API's | 0 | 1 | 200 |
0 | 40,673,779 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-11-18T09:36:00.000 | 0 | 1 | 0 | Pass a NAN in input vector for prediction | 40,673,629 | 1.2 | python,numpy,machine-learning,scikit-learn | You cannot use NaN values because the input vector will, for instance, be multiplied with a weight matrix. The result of such operations needs to be defined.
What you typically do if you have gaps in your input data is, depending on the specific type and structure of the data, fill the gaps with "artificial" values. Fo... | I have a classifier that has been trained using a given set of input training data vectors. There are missing values in the training data which is filled as numpy.Nan values and Using imputers to fill in the missing values.
However, In case of my input vector for prediction, how do I pass in the input where the value i... | 0 | 1 | 787 |
0 | 47,094,209 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2016-11-18T13:21:00.000 | 1 | 2 | 0 | ImportError: undefined symbol: _PyUnicodeUCS4_IsWhitespace | 40,678,200 | 0.099668 | python,numpy | I also got this error. If you google for it, you will find lot's of similar issues. The problem can happen when you have multiple Python versions. In my case, I had the Ubuntu 16.04 Python 2.7 via /usr/bin/python and another Python 2.7 via Linuxbrew. type python gave me /u/zeyer/.linuxbrew/bin/python2, i.e. the Linuxbr... | I am a python beginner and I would like some help with this. I am using Ubuntu and I had installed python using Anaconda, but then I tried to install it again using pip and now when I'm trying to run my code, at import numpy as np, I see this error
ImportError: /home/dev/.local/lib/python2.7/site-packages/numpy/core/m... | 0 | 1 | 3,200 |
0 | 40,687,262 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-18T22:14:00.000 | 0 | 1 | 0 | Algorithm/Approach to find destination node from source node of exactly k edges in undirected graph of billion nodes without cycle | 40,686,773 | 0 | python,algorithm,graph,shortest-path,breadth-first-search | As you've hinted, this will depend a lot on the data access characteristics of your system. If you were restricted to single-element accesses, then you'd be truly stuck, as trincot observes. However, if you can manage block accesses, then you have a chance of parallel operations.
However, I think that would be outsid... | Consider I have an adjacency list of billion of nodes structured using hash table arranged in the following manner:
key = source node
value = hash_table { node1, node2, node3}
The input values are from text file in the form of
from,to
1,2
1,5
1,11
... so on
eg.
key = '1'
value = {'2','5','11'}
means 1 is connected... | 0 | 1 | 105 |
0 | 44,625,370 | 0 | 1 | 0 | 0 | 1 | false | 113 | 2016-11-19T08:04:00.000 | 3 | 7 | 0 | Can Keras with Tensorflow backend be forced to use CPU or GPU at will? | 40,690,598 | 0.085505 | python,machine-learning,tensorflow,keras | I just spent some time figure it out.
Thoma's answer is not complete.
Say your program is test.py, you want to use gpu0 to run this program, and keep other gpus free.
You should write CUDA_VISIBLE_DEVICES=0 python test.py
Notice it's DEVICES not DEVICE | I have Keras installed with the Tensorflow backend and CUDA. I'd like to sometimes on demand force Keras to use CPU. Can this be done without say installing a separate CPU-only Tensorflow in a virtual environment? If so how? If the backend were Theano, the flags could be set, but I have not heard of Tensorflow flag... | 0 | 1 | 147,257 |
0 | 40,767,562 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2016-11-19T08:14:00.000 | 1 | 2 | 0 | Detecting shape of a contour and color inside | 40,690,675 | 0.099668 | python,opencv,colors,shape,contour | For finding the shape of a particular contour we can draw a bounded rectangle around the contour.
Now we can compare the area of contour with the area of bounded rectangle.
If area of contour is equal to half the area of bounded rectangle the shape is a triangle.
If the area of contour is less that area of bounded rect... | I am new to opencv using python and trying to get the shape of a contour in an image.
Considering only regular shapes like square, rectangle, circle and triangle is there any way to get the contour shape using only numpy and cv2 libraries?
Also i want to find the colour inside a contour. How can I do it?
For finding ar... | 0 | 1 | 5,369 |
0 | 40,713,966 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2016-11-20T12:22:00.000 | 2 | 1 | 0 | Creating azure ml experiments merely using python notebook within azure ml studio | 40,703,961 | 1.2 | python,azure,jupyter-notebook,azure-machine-learning-studio | The algorithms used as modules in Azure ML Studio are not currently able to be used directly as code for Python programming.
That being said: you can attempt to publish the outputs of the out-of-the-box algorithms as web services, which can be consumed by Python code in the Azure ML Studio notebooks. You can also creat... | I wonder if it is possible to design ML experiments without using the drag&drop functionality (which is very nice btw)? I want to use Python code in the notebook (within Azure ML studio) to access the algorithms (e.g., matchbox recommender, regression models, etc) in the studio and design experiments? Is this possible?... | 0 | 1 | 236 |
0 | 40,761,500 | 0 | 1 | 0 | 0 | 2 | false | 1 | 2016-11-20T13:07:00.000 | 0 | 2 | 0 | Generate a word search puzzle matrix | 40,704,339 | 0 | python,string,matrix,coordinates,words | I would rather look at words that are already there and then randomly select a word from the set of words that fit there.
Of course you might not fill the whole matrix like this. If you have put one word somewhere where it blocks all other words (no other word fits), you might have to backtrack, but that will kill the... | I'm not sure of the rules to create a matrix for a word search puzzle game. I am able to create a matrix with initial values of 0.
Is it correct that I'll randomly select a starting point(coordinates), and a random direction(horizontally,vertically,&,diagonally) for a word then manage if it would overlap with another w... | 0 | 1 | 936 |
0 | 40,761,650 | 0 | 1 | 0 | 0 | 2 | false | 1 | 2016-11-20T13:07:00.000 | 0 | 2 | 0 | Generate a word search puzzle matrix | 40,704,339 | 0 | python,string,matrix,coordinates,words | Start with the longest word.
First of all you must find all points and directions, where this word may fit. For example word 'WORD' may fit, when at the first pos there is NULL or W, on the second pos NULL or O, on the third NULL or R and on the fourth NULL or D.
Then You should group it to positions with no NULLS, wi... | I'm not sure of the rules to create a matrix for a word search puzzle game. I am able to create a matrix with initial values of 0.
Is it correct that I'll randomly select a starting point(coordinates), and a random direction(horizontally,vertically,&,diagonally) for a word then manage if it would overlap with another w... | 0 | 1 | 936 |
0 | 40,727,147 | 0 | 0 | 0 | 0 | 1 | true | 19 | 2016-11-21T18:22:00.000 | 15 | 1 | 0 | Difference between score and accuracy_score in sklearn | 40,726,899 | 1.2 | python,scikit-learn | In general, different models have score methods that return different metrics. This is to allow classifiers to specify what scoring metric they think is most appropriate for them (thus, for example, a least-squares regression classifier would have a score method that returns something like the sum of squared errors). I... | Whats the difference between score() method in sklearn.naive_bayes.GaussianNB() module and accuracy_score method in sklearn.metrics module? Both appears to be same. Is that correct? | 0 | 1 | 12,527 |
0 | 41,664,553 | 0 | 0 | 0 | 0 | 1 | false | 4 | 2016-11-22T07:51:00.000 | 1 | 3 | 0 | Difference of implementation between tensorflow softmax_cross_entropy_with_logits and sigmoid_cross_entropy_with_logits | 40,736,440 | 0.066568 | python,tensorflow | the major difference between sigmoid and softmax is that softmax function return result in terms of probability which is kind of more inline with the ML philosophy. Sum of all outputs from softmax result to 1. This is turn tells you how confident the network is about the answer.
Whereas, sigmoid outputs are discreet. I... | I recently came across tensorflow softmax_cross_entropy_with_logits, but I can not figure out what the difference on the implementation is compared to sigmoid_cross_entropy_with_logits. | 0 | 1 | 1,855 |
0 | 40,791,232 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2016-11-22T09:17:00.000 | 0 | 2 | 0 | Augementations in Keras ImageDataGenerator | 40,737,870 | 0 | python,tensorflow,keras,conv-neural-network | @Neal: Thank you for the prompt answer! You were right, I probably need to better explain my task. My work is somehow similar to classifying video sequences, but my data is saved in a database. I want my code to follow this steps for one epoch:
For i in (number_of_sequences):
Get N, the number of frames in the seque... | I have please two questions concerning the ImageDataGenerator:
1) Are the same augmentations used on the whole batch or each image gets its own random transformation?
e.g. for rotation, does the module rotates all the images in the batch with same angle or each image get a random rotation angle ?
2) The data in ImageDa... | 0 | 1 | 2,135 |
0 | 40,745,346 | 0 | 0 | 0 | 0 | 2 | true | 0 | 2016-11-22T12:41:00.000 | 1 | 2 | 0 | How to do GridSearchCV with train and test being different datasets? | 40,742,172 | 1.2 | python,machine-learning,scikit-learn,random-forest,data-science | If you can, you may simply merge the two datasets and perform GridSearchCV, this ensures the generalization ability to the other dataset. If you are talking about generalization to future unknown dataset, then this might not work, because there isn't a perfect dataset from which we can train a perfect model. | I would like to find the best parameters for a RandomForest classifier (with scikit-learn) in a way that it generalises well to other datasets (which may not be iid).
I was thinking doing grid search using the whole training dataset while evaluating the scoring function on other datasets.
Is there an easy to do this in... | 0 | 1 | 1,071 |
0 | 40,744,270 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2016-11-22T12:41:00.000 | 2 | 2 | 0 | How to do GridSearchCV with train and test being different datasets? | 40,742,172 | 0.197375 | python,machine-learning,scikit-learn,random-forest,data-science | I don't think you can evaluate on a different data set. The whole idea behind GridSearchCV is that it splits your training set into n folds, trains on n-1 of those folds and evaluates on the remaining one, repeating the procedure until every fold has been "the odd one out". This keeps you from having to set apart a spe... | I would like to find the best parameters for a RandomForest classifier (with scikit-learn) in a way that it generalises well to other datasets (which may not be iid).
I was thinking doing grid search using the whole training dataset while evaluating the scoring function on other datasets.
Is there an easy to do this in... | 0 | 1 | 1,071 |
0 | 40,745,068 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-11-22T14:30:00.000 | 0 | 2 | 0 | Centroid of a set of positions on a toroidally wrapped (x- and y- wrapping) 2D array? | 40,744,501 | 0 | python,algorithm,math | I've done this before for Ramachandran plots. I will check but I seem to remember the algorithm I used was:
Assume that your Euclidian area has an origin at (0,0)
Shift all the points in a set so that they are fully within the area
Calculate the centroid
Shift the centroid back by the reverse of the original
So, say ... | I have a flat Euclidean rectangular surface but when a point moves to the right boundary, it will appear at the left boundary (at the same y value). And visa versa for moving across the top and bottom boundaries. I'd like to be able to calculate the centroid of a set of points. The set of points in question are mostly ... | 0 | 1 | 116 |
0 | 60,201,018 | 0 | 0 | 0 | 0 | 2 | false | 26 | 2016-11-22T17:06:00.000 | 2 | 4 | 0 | keras: what is the difference between model.predict and model.predict_proba | 40,747,679 | 0.099668 | python,machine-learning,deep-learning,keras | Just a remark : In fact you have both predict and predict_proba in most classifiers (in Scikit for example). As already mentioned, the first one predicts the class, the second one provides probabilities for each class, classified in ascending order. | I found model.predict and model.predict_proba both give an identical 2D matrix representing probabilities at each categories for each row.
What is the difference of the two functions? | 0 | 1 | 47,190 |
0 | 68,835,517 | 0 | 0 | 0 | 0 | 2 | false | 26 | 2016-11-22T17:06:00.000 | 0 | 4 | 0 | keras: what is the difference between model.predict and model.predict_proba | 40,747,679 | 0 | python,machine-learning,deep-learning,keras | In the recent version of keras e.g. 2.6.0, predict and predict_proba is same i.e. both give probabilities. To get the class labels use predict_classes. The documentation is not updated | I found model.predict and model.predict_proba both give an identical 2D matrix representing probabilities at each categories for each row.
What is the difference of the two functions? | 0 | 1 | 47,190 |
0 | 40,750,775 | 0 | 0 | 0 | 0 | 1 | true | 8 | 2016-11-22T20:05:00.000 | 16 | 1 | 0 | Prevent pandas.read_csv from inferring dtypes | 40,750,670 | 1.2 | python,pandas | Use the parameter dtype=object for Pandas to keep the data as such at load time. | How to prevent pandas.read_csv() from inferring the data types. For example, its converting strings true and false to Bool: True and False. The columns are many for many files, therefore not feasible to do:
df['field_name'] = df['field_name'].astype(np.float64) for each of the columns in each file. I prefer pandas to j... | 0 | 1 | 3,832 |
0 | 40,759,746 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-11-23T08:24:00.000 | 0 | 1 | 0 | tflearn: different number of rows of each input | 40,759,257 | 1.2 | python,tensorflow,tflearn | First of all, you have to know what exactly is your training sample. I am not sure what you mean by "input", does one input means one sample? Or does one row in your input means one sample?
If one input means one sample, you are in some trouble, because almost all CNN (and almost any other machine learning algos) requi... | I am using tflearn for modeling CNN.
However, my data has different number of rows in each input (but same number of columns).
For example, I have 100 inputs.
The first input's dimension is 4*9 but the second and third have 1*9.
I do not sure how to feed and shape data by using input_data(). | 0 | 1 | 57 |
0 | 40,767,005 | 0 | 0 | 0 | 0 | 1 | true | 28 | 2016-11-23T14:18:00.000 | 33 | 4 | 0 | Suggestions to plot overlapping lines in matplotlib? | 40,766,909 | 1.2 | python,matplotlib | Just decrease the opacity of the lines so that they are see-through. You can achieve that using the alpha variable. Example:
plt.plot(x, y, alpha=0.7)
Where alpha ranging from 0-1, with 0 being invisible. | Does anybody have a suggestion on what's the best way to present overlapping lines on a plot? I have a lot of them, and I had the idea of having full lines of different colors where they don't overlap, and having dashed lines where they do overlap so that all colors are visible and overlapping colors are seen.
But stil... | 0 | 1 | 35,477 |
0 | 40,768,770 | 0 | 1 | 0 | 0 | 1 | false | 2 | 2016-11-23T15:40:00.000 | 0 | 2 | 0 | How to sort with incomplete ordering? | 40,768,575 | 0 | python,algorithm,sorting | The built-in sort method requires that cmp imposes a total ordering. It doesn't work if the comparisons are inconsistent. If it returns that A < B one time it must always return that, and it must return that B > A if the arguments are reversed.
You can make your cmp implementation work if you introduce an arbitrary tie... | I have a list of elements to sort and a comparison function cmp(x,y) which decides if x should appear before y or after y. The catch is that some elements do not have a defined order. The cmp function returns "don't care".
Example: Input: [A,B,C,D], and C > D, B > D. Output: many correct answers, e.g. [D,C,B,A] or [A,D... | 0 | 1 | 809 |
0 | 40,770,869 | 0 | 1 | 0 | 0 | 1 | true | 1 | 2016-11-23T17:25:00.000 | 4 | 1 | 0 | Iterative Divide and Conquer algorithms | 40,770,767 | 1.2 | python,algorithm,computer-science | The cheap way to turn any recursive algorithm into an iterative algorithm is to take the recursive function, put it in a loop, and use your own stack. This eliminates the function call overhead and from saving any unneeded data on the stack. However, this is not usually the "best" approach ("best" depends on the proble... | I am trying to create an algorithm using the divide-and-conquer approach but using an iterative algorithm (that is, no recursion).
I am confused as to how to approach the loops.
I need to break up my problems into smaller sub problems, until I hit a base case. I assume this is still true, but then I am not sure how I c... | 0 | 1 | 2,428 |
0 | 40,777,110 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-24T02:01:00.000 | 1 | 1 | 0 | How to find initial guess for leastsq function in Python? | 40,776,967 | 0.197375 | python,numpy,least-squares,data-science | No, you can't really calculate an initial guess.
You'll just have to make an educated guess, and that really depends on your data and model.
If the initial guess affects the fitting, there is likely something else going on; you're probably getting stuck in local minima. Your model may be too complex, or your data range... | I have a dataset for which I need to fit the plot. I am using leastsq() for fitting. However, currently I need to give initial guess values manually which is really affecting the fitting. Is there any way to first calculate the initial guess values which I can pass in leastsq()? | 0 | 1 | 481 |
0 | 40,782,596 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-24T08:33:00.000 | -1 | 1 | 0 | In spark, what kind of transformations necessit a shuffling (can imply a new stage)? | 40,781,351 | -0.197375 | python,scala,hadoop,apache-spark,rdd | There are some common operator like reducebykey,groupbykey that can cause to shuffle.You can visit spark offical website to learn more. | How to determine if a new transformation in spark causes the creation of a stage? is there a list of them ? | 0 | 1 | 56 |
0 | 40,781,778 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-11-24T08:44:00.000 | 1 | 1 | 0 | Generate random values given 2d pdf | 40,781,553 | 1.2 | python,scipy | rv_continuous only handles univariate distributions. | How to use rv_continuous for multidimensional pdf?
Is rv_continuous able to handle multidimensional pdf?
My goal is to use the rvs method to generate random values from an arbitrary 2d distribution. Do you know any other solutions? | 0 | 1 | 152 |
0 | 40,793,242 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2016-11-24T14:09:00.000 | 1 | 3 | 0 | An Algorithm to Determine How Similar Two Sentences Are | 40,788,494 | 0.066568 | python,algorithm,parsing,tree,nlp | Assigning weights is a million dollar question there. As a first step, I would identify parts of the sentence (subject-predicate-clause, etc) and the sentence structure (simple-compound-complex, etc) to find "anchor" words that would have highest weight. That should make the rest of the task easier. | A friend of mine had an idea to make a speed reading program that displays words one by one (much like currently existing speed reading programs). However, the program would filter out words that aren't completely necessary to the meaning (if you want to skim something).
I have starting to implement this program, but ... | 0 | 1 | 3,305 |
0 | 40,800,749 | 0 | 0 | 0 | 0 | 1 | false | 5 | 2016-11-24T20:08:00.000 | 3 | 4 | 0 | Index Python List with Numpy Boolean Array | 40,793,882 | 0.148885 | python,numpy | map(x.__getitem__,np.where(mask)[0])
Or if you want list comprehension
[x[i] for i in np.where(mask)[0]]
This keeps you from having to iterate over the whole list, especially if mask is sparse. | Is there a way to index a python list like x = ['a','b','c'] using a numpy boolean array? I'm currently getting the following error: TypeError: only integer arrays with one element can be converted to an index | 0 | 1 | 11,235 |
0 | 40,795,462 | 0 | 0 | 0 | 0 | 1 | false | 12 | 2016-11-24T20:36:00.000 | 1 | 2 | 0 | Upload a CSV file and read it in Bokeh Web app | 40,794,180 | 0.099668 | javascript,python,file,upload,bokeh | As far as I know there is no widget native to Bokeh that will allow a file upload.
It would be helpful if you could clarify your current setup a bit more. Are your plots running on a bokeh server or just through a Python script that generates the plots?
Generally though, if you need this to be exposed through a browser... | I have a Bokeh plotting app, and I need to allow the user to upload a CSV file and modify the plots according to the data in it.
Is it possible to do this with the available widgets of Bokeh?
Thank you very much. | 1 | 1 | 6,493 |
0 | 44,195,108 | 0 | 0 | 0 | 0 | 1 | false | 4 | 2016-11-26T15:31:00.000 | 2 | 2 | 0 | Keras and cross validation | 40,819,939 | 0.197375 | python,scikit-learn,keras,cross-validation | Nope... That seem to be the solution. (Of what I know of.) | I am currently trying train a regression network using keras. To ensure I proper training I've want to train using crossvalidation.
The Problem is that it seems that keras don't have any functions supporting crossvalidation or do they?
The only solution I seemed to have found is to use scikit test_train_split and ru... | 0 | 1 | 4,491 |
0 | 40,821,227 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-11-26T17:33:00.000 | 0 | 2 | 0 | How to use Blackman Window in numpy to take a part of values from an array? | 40,821,099 | 0 | python,arrays,numpy | Assuming your array a is 1D and its length is a multiple a 500, a simple np.sum(a.reshape((-1, 500)) ** 2, axis=1) would suffice. If you want a more complicated operation, please edit your question accordingly. | I want to take a part of values (say 500 values) of an array and perform some operation on it such as take sum of squares of those 500 values. and then proceed with the next 500 values of the same array.
How should I implement this? Would a blackman window be useful in this case or is another approach more suitable? | 0 | 1 | 410 |
0 | 41,367,703 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2016-11-26T20:01:00.000 | 0 | 2 | 0 | Does Sklearn.LogisticRegression add a column of 1? | 40,822,563 | 0 | python,scikit-learn | This is the default behavior since fit_intercept defaults to True | I have been told that when dealing with multivariate logistic regression, you want to add a column of 1's in the X matrix for the model intercept. When using the model from sklearn, does the module automatically add the column of 1? | 0 | 1 | 2,238 |
0 | 40,829,189 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-11-26T21:27:00.000 | 3 | 1 | 0 | resetting c_t and h_t in LSTM every couple of sequences in RNN | 40,823,341 | 1.2 | python,neural-network,tensorflow,recurrent-neural-network,lstm | I don't think you would want to do that since each example in the batch should be independent. If they are not you should just have a batch size of 1 and a sequence of length 100 * batch_size. Often you might want to save the state in between batches. In this case you would need to save the RNN state to a variable o... | I am trying to implement RNN in Tensorflow for text prediction. I am using BasicLSTMCell for this purpose, with sequence lengths of 100.
If I understand correctly, the output activation h_t and the activation c_t of the LSTM are reset each time we enter a new sequence (that is, they are updated 100 times along the seq... | 0 | 1 | 228 |
0 | 40,836,970 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-28T03:54:00.000 | 2 | 1 | 0 | Using primary key column as index values in Pandas dataframe - Best practice? | 40,836,932 | 0.379949 | python,pandas,dataframe | It depends on your application. The one "best practice" I know of for Pandas indexes is to keep their values unique. As for which column to use as your index, that depends on what you will later do with the DataFrame (e.g. what other data you might merge it with). | I'm relatively new to Pandas and I wanted to know best practices around incremental vs. primary key indexes.
In particular, I'm wondering:
what are the benefits of using a dataset's primary key as its DataFrame index, as opposed to just sticking with the default incremental integer index?
are any potential pitfalls fo... | 0 | 1 | 1,018 |
0 | 40,842,347 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2016-11-28T09:18:00.000 | 4 | 1 | 0 | ValueError: cannot compute LDA over an empty collection (no terms) | 40,840,731 | 1.2 | python,gensim,lda,topic-modeling | Finally figured it out. The issue with small documents is that if you try to filter the extremes from dictionary, you might end up with empty lists in corpus.corpus = [dictionary.doc2bow(text)].
So the values of parameters in dictionary.filter_extremes(no_below=2, no_above=0.1) needs to be selected accordingly and care... | Getting this error in python when trying to compute lda for a smaller size of corpus but works fine in other cases.
The size of corpus is 15 and I tried setting the number of topic to 5 then reduced it to 2 but it still gives the same error : ValueError: cannot compute LDA over an empty collection (no terms)
getting er... | 0 | 1 | 5,310 |
0 | 40,842,338 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-11-28T09:18:00.000 | 0 | 1 | 1 | Config the opencv in python 2.7 of MacOS | 40,840,738 | 0 | python,macos,opencv | copy cv2.so and cv.py to /System/Library/Frameworks/Python.framework/Versions/2.7/lib/
You can find this two files in /usr/local/Cellar/opecv/../lib/python2.7 | Today I was installing the opencv by
homebrew install opencv
and then I try to import it by:
python
import cv2
and it return: No moudule named cv2
However, I try to import it by:
Python3
Import cv2
it works well.
I tried to install opencv again but homebrew said it has been installed.
Dont know what can I do now | 0 | 1 | 30 |
0 | 47,368,406 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-28T10:20:00.000 | 0 | 1 | 0 | is it required to install TF Slim externally after pulling a docker image latest-devel-gpu(r0.10) | 40,841,872 | 0 | python,tensorflow | OP self-solved problem by upgrading to the docker tag 0.10.0-devel-gpu (and it should work going forward). | Is it required to install TF Slim externally after pulling a docker image latest-devel-gpu(r0.10) for GPU environment?
when I do import tensorflow.contrib.slim as slim it gives me an error:
"No module named Slim" through python. | 0 | 1 | 939 |
0 | 40,844,025 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-11-28T12:01:00.000 | 2 | 2 | 0 | How to match images taken at different angles | 40,843,794 | 0.197375 | python,opencv,image-processing | The state of the art for such object recognition tasks are convolutional neural networks, but you will need a large labelled training set, which might rule that out. Otherwise SIFT/SURF is probably what you are looking for. They are pretty robust towards most transformations. | I have multiple images of same object taken at different angles and has many such objects. I need to match a test image which is taken at a random angle later belongs to particular object with similar background, by matching it with those images. The objects are light installations inside a building. Same object may ... | 0 | 1 | 1,892 |
0 | 40,852,542 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-11-28T19:58:00.000 | 2 | 1 | 0 | Smallest total weight in weighted directed graph | 40,852,458 | 1.2 | python,algorithm,graph | 4 is the correct one!, because all edges have the same weight, so you need to find the node traversing the minimum quantity of edges.
1 Is wrong because depth-first search doesn't consider edge weights, so any node could be reached first | There is question from one Quiz that I don't fully understand:
Suppose you have a weighted directed graph and want to find a path between nodes A and B with the smallest total weight. Select the most accurate statement:
If some edges have negative weights, depth-first search finds a correct solution.
If all edges have... | 0 | 1 | 826 |
0 | 40,870,126 | 0 | 0 | 0 | 0 | 1 | true | 31 | 2016-11-29T12:37:00.000 | 46 | 2 | 0 | Difference between Dense and Activation layer in Keras | 40,866,124 | 1.2 | python,machine-learning,neural-network,deep-learning,keras | Using Dense(activation=softmax) is computationally equivalent to first add Dense and then add Activation(softmax). However there is one advantage of the second approach - you could retrieve the outputs of the last layer (before activation) out of such defined model. In the first approach - it's impossible. | I was wondering what was the difference between Activation Layer and Dense layer in Keras.
Since Activation Layer seems to be a fully connected layer, and Dense have a parameter to pass an activation function, what is the best practice ?
Let's imagine a fictionnal network like this :
Input -> Dense -> Dropout -> Final ... | 0 | 1 | 11,235 |
0 | 40,871,454 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-11-29T16:52:00.000 | 1 | 1 | 0 | GradientBoostingClassifier and many columns | 40,871,371 | 1.2 | python,classification,gbm | You can replace the binary variable with the actual country name then collapse all of these columns into one column. Use LabelEncoder on this column to create a proper integer variable and you should be all set. | I use GradientBoosting classifier to predict gender of users. The data have a lot of predictors and one of them is the country. For each country I have binary column. There are always only one column set to 1 for all country columns. But such desicion is very slow from computation point of view. Is there any way to rep... | 0 | 1 | 69 |
0 | 72,478,813 | 0 | 1 | 0 | 0 | 1 | false | 22 | 2016-11-29T18:01:00.000 | 0 | 4 | 0 | Unable to install cv2 on windows | 40,872,683 | 0 | python,opencv | So I was using PyCharm, and what worked for me was to install it directly from file->settings, Project:your-project-name->Python Interpreter list | I am trying to install opencv in python on my windows machine but I am unable to do so. I have python 2.7.11::Anaconda 2.4.1 <32-bit>
Here is what I have tried till now -
pip install cv2 on command line gives the error :
could not find a
version that satisfies the requirement cv2
I downloaded the package from sou... | 0 | 1 | 60,085 |
0 | 58,649,705 | 0 | 0 | 0 | 0 | 3 | false | 14 | 2016-11-29T23:37:00.000 | 3 | 9 | 0 | EOFError: Compressed file ended before the end-of-stream marker was reached - MNIST data set | 40,877,781 | 0.066568 | python,tensorflow | It is very simple in windows :
Go to : C:\Users\Username\.keras\datasets
and then Delete the Dataset that you want to redownload or has the error | I am getting the following error when I run mnist = input_data.read_data_sets("MNIST_data", one_hot = True).
EOFError: Compressed file ended before the end-of-stream marker was reached
Even when I extract the file manually and place it in the MNIST_data directory, the program is still trying to download the file inst... | 0 | 1 | 52,865 |
0 | 46,372,714 | 0 | 0 | 0 | 0 | 3 | false | 14 | 2016-11-29T23:37:00.000 | 19 | 9 | 0 | EOFError: Compressed file ended before the end-of-stream marker was reached - MNIST data set | 40,877,781 | 1 | python,tensorflow | This is because for some reason you have an incomplete download for the MNIST dataset.
You will have to manually delete the downloaded folder which usually resides in ~/.keras/datasets or any path specified by you relative to this path, in your case MNIST_data.
Perform the following steps in the terminal (ctrl + alt +... | I am getting the following error when I run mnist = input_data.read_data_sets("MNIST_data", one_hot = True).
EOFError: Compressed file ended before the end-of-stream marker was reached
Even when I extract the file manually and place it in the MNIST_data directory, the program is still trying to download the file inst... | 0 | 1 | 52,865 |
0 | 58,121,301 | 0 | 0 | 0 | 0 | 3 | false | 14 | 2016-11-29T23:37:00.000 | 1 | 9 | 0 | EOFError: Compressed file ended before the end-of-stream marker was reached - MNIST data set | 40,877,781 | 0.022219 | python,tensorflow | I had the same issue when downloading datasets using torchvision on Windows. I was able to fix this by deleting all files from the following path:
C:\Users\UserName\MNIST\raw | I am getting the following error when I run mnist = input_data.read_data_sets("MNIST_data", one_hot = True).
EOFError: Compressed file ended before the end-of-stream marker was reached
Even when I extract the file manually and place it in the MNIST_data directory, the program is still trying to download the file inst... | 0 | 1 | 52,865 |
0 | 60,873,472 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-11-30T00:36:00.000 | 0 | 4 | 0 | what is this color map? cmap=mglearn.cm3 | 40,878,325 | 0 | python,pandas,anaconda | conda install pip(because conda install mglearn will give a error)
pip install mglearn
grr = pd.plotting.scatter_matrix( ...., cmap=mglearn.cm3)
if you still can't see the output then you might have missed %matplotlib inline | I try to run the following code but it gives the following error in recognistion of mglearn color map.
grr = pd.scatter_matrix( ...., cmap=mglearn.cm3)
ErrorName: name 'mglearn' is not defined
I should add pd is Anaconda Pandas package imported as pd but does not recognize the color map mglearn.cm3
Any suggestions? | 0 | 1 | 5,864 |
0 | 40,947,457 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-11-30T11:48:00.000 | 0 | 1 | 0 | Multi-Output Classification using scikit Decision Trees | 40,887,631 | 0 | python,machine-learning,scikit-learn | Regarding Problem 1, if you expect the different components of the target value to be independent, you can approach the problem as building a classifier for every component. That is, if the features are F = (F_1, F_2, ..., F_N) and the targets Y = (Y_1, Y_2, ..., Y_N), create a classifier with features F and target Y_1... | Disclaimer: I'm new to the field of Machine Learning, and even though I have done my fair share of research during the past month I still lack deep understanding on this topic.
I have been playing around with the scikit library with the objective of learning how to predict new data based on historic information, and cl... | 0 | 1 | 903 |
0 | 43,810,812 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-12-01T06:58:00.000 | 0 | 1 | 0 | Finding the accuracy of an HMM model for POS-Tagger | 40,904,368 | 0 | python,machine-learning,nlp,pos-tagger | To calculate accuracy on any dataset, count the number of words (excluding start and stop symbols) that you tagged correctly, and divide by the total number of words (excluding start and stop symbols). | I am implementing the Viterbi Algorithm for POS-Tagger using the Brown-corpus as my data set. Now an important aspect of this NLP task is finding the accuracy of the model. So I really need help as what to implement. It's easier using the nltk toolkit but since I am not using a toolkit, I am stuck on how to determine t... | 0 | 1 | 582 |
0 | 40,907,857 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2016-12-01T10:07:00.000 | 1 | 1 | 0 | What is the underlying difference between multiplying a matrix and looping through? | 40,907,731 | 1.2 | python,performance,numpy,processing-efficiency | Yes, both approaches will have to loop over the values in the two matrices. However, python is dynamically typed such that the body of the loop needs to check the types of the three indices used for iteration, ensure that indexing the two input matrices is supported, determine the type of the values extracted from the ... | When you have two numPy matrix, you could call a dot function to multiply them. Or you could loop through each manually and multiply each value manually. Why and what is the speed difference ? Surely dot function still has to do that but Lower level? | 0 | 1 | 35 |
1 | 42,197,304 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-12-02T04:57:00.000 | 1 | 1 | 0 | How can I integrate my Python based OpenCV project in Android? | 40,925,079 | 1.2 | android,python,opencv,image-processing | This process can be done via NDK & OpenCV with Java support in Android Studio.
You need to compile NDK libraries and generate .so files. Then the project will work. | I am working on my OpenCV project with Python which basically recognizes hand gestures. I want to do the same using Android. Is it possible? Is there any way to do so?
I want to recognize very basic hand gestures using my Android device. Is it possible with Python & OpenCV with Android? Also share any other way possibl... | 0 | 1 | 220 |
0 | 40,942,885 | 0 | 0 | 0 | 0 | 2 | false | 2 | 2016-12-02T23:25:00.000 | 2 | 2 | 0 | ML model to predict rankings (arbitrary ordering of a list) | 40,942,391 | 0.197375 | python,machine-learning | Since there's an unknown function that generates the output, it's a regression problem. Neural network with 2 hidden layers and e.g. sigmoid can learn any arbitrary function. | I need some orientation for a problem I’m trying to solve. Anything would be appreciated, a keyword to Google or some indication !
So I have a list of 5 items. All items share the same features, let’s say each item has 3 features for the example.
I pass the list to a ranking function which takes into account the featur... | 0 | 1 | 1,288 |
0 | 40,947,117 | 0 | 0 | 0 | 0 | 2 | true | 2 | 2016-12-02T23:25:00.000 | 1 | 2 | 0 | ML model to predict rankings (arbitrary ordering of a list) | 40,942,391 | 1.2 | python,machine-learning | It could be treated as a regression problem with the following trick: You are given 5 items with 5 feature vectors and the "black box" function outputs 5 distinct scores as [1, 2, 3, 4, 5]. Treat these as continuous values. So, you can think of your function as operating by taking five distinct input vectors x1, x2, x3... | I need some orientation for a problem I’m trying to solve. Anything would be appreciated, a keyword to Google or some indication !
So I have a list of 5 items. All items share the same features, let’s say each item has 3 features for the example.
I pass the list to a ranking function which takes into account the featur... | 0 | 1 | 1,288 |
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