https://github.com/Microsoft/CNTK
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Tip revision: e0dc3186673cb478283f122aa6bed7863cc8622b authored by Nikos Karampatziakis on 21 November 2016, 16:46:23 UTC
Merge branch 'master' into nikosk/lrdisplay
Tip revision: e0dc318
create_data.py
# -*- coding: utf-8 -*-
"""
Copyright (c) Microsoft. All rights reserved.
Licensed under the MIT license. See LICENSE file in the project root for full license information.

"""

import numpy as np
from sklearn.utils import shuffle

# number of dimensions
Dim = 2

# number of samples
N_train = 1000
N_test = 500

def generate(N, mean, cov, diff):   
    #import ipdb;ipdb.set_trace()
    num_classes = len(diff)
    samples_per_class = int(N/2)

    X0 = np.random.multivariate_normal(mean, cov, samples_per_class)
    Y0 = np.zeros(samples_per_class)
    
    for ci, d in enumerate(diff):
        X1 = np.random.multivariate_normal(mean+d, cov, samples_per_class)
        Y1 = (ci+1)*np.ones(samples_per_class)
    
        X0 = np.concatenate((X0,X1))
        Y0 = np.concatenate((Y0,Y1))

    X, Y = shuffle(X0, Y0)
    
    return X,Y

def create_data_files(num_classes, diff, train_filename, test_filename, regression):
    print("Outputting %s and %s"%(train_filename, test_filename))
    mean = np.random.randn(num_classes)
    cov = np.eye(num_classes)      
    
    for filename, N in [(train_filename, N_train), (test_filename, N_test)]:
        X, Y = generate(N, mean, cov, diff)
        
        # output in CNTK Text format
        with open(filename, "w") as dataset:
            num_labels = int((1 + np.amax(Y)))
            for i in range(N):
                dataset.write("|features ")
                for d in range(Dim):
                    dataset.write("%f " % X[i,d])
                if (regression): 
                    dataset.write("|labels %f\n" % Y[i])
                else:
                    labels = ['0'] * num_labels;
                    labels[int(Y[i])] = '1'
                    dataset.write("|labels %s\n" % " ".join(labels))

def main():
    # random seed (create the same data)
    np.random.seed(10)

    create_data_files(Dim, [3.0], "Train_cntk_text.txt", "Test_cntk_text.txt", True)
    create_data_files(Dim, [[3.0], [3.0, 0.0]], "Train-3Classes_cntk_text.txt", "Test-3Classes_cntk_text.txt", False)
    
if __name__ == '__main__':
    main()
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