swh:1:snp:f50ab94432af916b5fb8b4ad831e8dddded77084
Raw File
Tip revision: 02b3640ac9c1c3abe0302bcf9f51371c1d4c0f6a authored by Emad Barsoum on 12 October 2017, 23:40:18 UTC
Merge branch 'atanh' of https://github.com/lakshayg/CNTK into lakshayg-atanh
Tip revision: 02b3640
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()
back to top