November 2017
Intermediate to advanced
304 pages
6h 58m
English
Content preview from Machine Learning with TensorFlow 1.x
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Extracting features
Now, we will extract the required features to train and test our data:
feature_columns = ['SNPS_log_return_positive', 'SNPS_log_return_negative'] for i in range(len(codes)): index = codes[i].split("/")[1] feature_columns.extend([ '{}_log_return_1'.format(index), '{}_log_return_2'.format(index), '{}_log_return_3'.format(index) ]) features_and_labels = pd.DataFrame(columns=feature_columns) closings['SNPS_log_return_positive'] = 0 closings.ix[closings['SNPS_log_return'] >= 0, 'SNPS_log_return_positive'] = 1 closings['SNPS_log_return_negative'] = 0 closings.ix[closings['SNPS_log_return'] < 0, 'SNPS_log_return_negative'] = 1 for i in range(7, len(closings)): feed_dict = {'SNPS_log_return_positive': closings['SNPS_log_return_positive'].ix[i], ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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