classification

问题 First of all I wanna say that I am a python beginner and also completely new to neural networks. When I read about it I was very excited and thought I set up a little code from scratch (see code below). But somehow my code is not working properly. I guess there are some major bugs (in the algorithm and the programming?). But I cannot find them at the moment. So, in the handwritten notes you can see my system (and some formulas). I wanna solve a decision problem where I have data in the form

问题 I am trying to extend this answer to knn classifier: load fisheriris; % // convert species to double isnum = cellfun(@isnumeric,species); result = NaN(size(species)); result(isnum) = [species{isnum}]; % // Crossvalidation vals = crossval(@(XTRAIN, YTRAIN, XTEST, YTEST)fun_knn(XTRAIN, YTRAIN, XTEST, YTEST), meas, result); the fun_knn funcion is: function testval = fun_knn(XTRAIN, YTRAIN, XTEST, YTEST) yknn = knnclassify(XTEST, XTRAIN, YTRAIN); [~,classNet] = max(yknn,[],2); [~,classTest] = max

问题 I have a set of categorical columns (strings), that I'm parsing and converting into Vectors of features to pass to a mllib classifier (random forest). In my input data, some columns have null values. Say, in one of those columns, I have p values + a null value : How should I build my feature Vectors, and the categoricalFeaturesInfo map of the classifier ? option 1 : I tell p values in categoricalFeaturesInfo, and I use Double.NaN in my input Vectors ? side question : How NaNs are handled by

问题 I'm currently using a resnet built in keras to do two class classification. I am using model checkpoint to save the best models based off of validation accuracy. Better and better models are saved until I go through all my datapoints a few times. Keras keeps saving new models showing they have higher accuracy but when I test the models they perform worse than previous models. Here is an output of testing each model with validation data. The first number in the model name is the epoch, the

问题 I'm currently using various classifiers in Weka. My testing data is labelled, e.g.: @relation bmwreponses @attribute IncomeBracket {0,1,2,3,4,5,6,7} @attribute FirstPurchase numeric @attribute LastPurchase numeric @attribute responded {1,0} @data 4,200210,200601,0 5,200301,200601,1 6,200411,200601,0 5,199609,200603,0 6,200310,200512,1 ... The last value per row is the class element, i.e. responded. But if I try unlabelled test data, e.g.: @relation bmwreponses @attribute IncomeBracket {0,1,2

问题 I have a set of data (cost & distance) I want to aggregate those ns classes depending on the distance and find the sum of the cost for the aggregated data. Here are some example tables. Nam Cost distance 1 1005 10 2 52505 52 3 51421 21 4 651 10 5 656 0 6 5448 1 Classes Class From To 1 0 5 2 5 15 3 15 100 Result Class Sum 1 6104 2 1656 3 103926 I am doing this but it takes a lot of time to process. I sure that there is a better way to do it for (i in 1:6) { for (j in 1:3) { if((Table_numbers[i

问题 I'm trying to implement the Minimum Distance Algorithm for image classification using GDAL and Python. After calculating the mean pixel-value of the sample areas and storing them into a list of arrays ("sample_array"), I read the image into an array called "values". With the following code I loop through this array: values = valBD.ReadAsArray() # loop through pixel columns for X in range(0,XSize): # loop thorugh pixel lines for Y in range (0, YSize): # initialize variables minDist = 9999 #

问题 I am using GridSearchCV to do classification and my codes are: parameter_grid_SVM = {'dual':[True,False], 'loss':["squared_hinge","hinge"], 'penalty':["l1","l2"] } clf = GridSearchCV(LinearSVC(),param_grid=parameter_grid_SVM,verbose=2) clf.fit(trian_data, labels) And then, I meet the error ValueError: Unsupported set of arguments: penalty='l1' is only supported when dual='false'., Parameters: penalty='l1', loss='hinge', dual=False later on I change my code to : clf = GridSearchCV(LinearSVC

问题 I'm trying to use FeatureUnion to extract different features from a datastructure, but it fails due to different dimensions: ValueError: blocks[0,:] has incompatible row dimensions Implementaion My FeatureUnion is built the following way: features = FeatureUnion([ ('f1', Pipeline([ ('get', GetItemTransformer('f1')), ('transform', vectorizer_f1) ])), ('f2', Pipeline([ ('get', GetItemTransformer('f2')), ('transform', vectorizer_f1) ])) ]) GetItemTransformer is used to get different parts of

问题 i got data-set like this i need to analyse and predict the status column. This is just 2 entrees from the training data set. In this data set there is heart rate pattern(which is collected in 1 second intervals, 10 numbers altogether) its a time series array(correct me if i'm wrong) i just need to know best way to analyse and get a prediction using this data. I'm using scikit-learning for my data-mining and machine learning. What i just want to know is what is the best way to analyse these