# Example of solving multivariate linear regression in Python. # # Uses only Numpy, with Matplotlib for plotting. # # Eli Bendersky (http://eli.thegreenplace.net) # This code is in the public domain from __future__ import print_function import csv import matplotlib.pyplot as plt import numpy as np from timer import Timer def read_CCPP_data(filename): “””Read data from the given CCPP CSV file. Returns (data, header). data is a 2D Numpy array of type np.float32, with a sample per row. header is the names of the columns as read from the CSV file. “”” with open(filename, ‘rb’) as file: reader = csv.reader(file) header = reader.next() return np.array(list(reader), dtype=np.float32), header def feature_normalize(X): “””Normalize the feature matrix X. Given a feature matrix X, where each row is a vector of features, normalizes each feature. Returns (X_norm,…

Link to Full Article: eliben/deep-learning-samples

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