Merge pull request #92 from yashLadha/master

Added Linear regression

machine_learning/linear_regression.py

@@ -0,0 +1,108 @@
+"""
+Linear regression is the most basic type of regression commonly used for
+predictive analysis. The idea is preety simple, we have a dataset and we have
+a feature's associated with it. The Features should be choose very cautiously
+as they determine, how much our model will be able to make future predictions.
+We try to set these Feature weights, over many iterations, so that they best
+fits our dataset. In this particular code, i had used a CSGO dataset (ADR vs
+Rating). We try to best fit a line through dataset and estimate the parameters.
+"""
+
+import requests
+import numpy as np
+
+
+def collect_dataset():
+    """ Collect dataset of CSGO
+    The dataset contains ADR vs Rating of a Player
+    :return : dataset obtained from the link, as matrix
+    """
+    response = requests.get('https://raw.githubusercontent.com/yashLadha/' +
+                            'The_Math_of_Intelligence/master/Week1/ADRvs' +
+                            'Rating.csv')
+    lines = response.text.splitlines()
+    data = []
+    for item in lines:
+        item = item.split(',')
+        data.append(item)
+    data.pop(0)  # This is for removing the labels from the list
+    dataset = np.matrix(data)
+    return dataset
+
+
+def run_steep_gradient_descent(data_x, data_y,
+                               len_data, alpha, theta):
+    """ Run steep gradient descent and updates the Feature vector accordingly_
+    :param data_x   : contains the dataset
+    :param data_y   : contains the output associated with each data-entry
+    :param len_data : length of the data_
+    :param alpha    : Learning rate of the model
+    :param theta    : Feature vector (weight's for our model)
+    ;param return    : Updated Feature's, using
+                       curr_features - alpha_ * gradient(w.r.t. feature)
+    """
+    n = len_data
+
+    prod = np.dot(theta, data_x.transpose())
+    prod -= data_y.transpose()
+    sum_grad = np.dot(prod, data_x)
+    theta = theta - (alpha / n) * sum_grad
+    return theta
+
+
+def sum_of_square_error(data_x, data_y, len_data, theta):
+    """ Return sum of square error for error calculation
+    :param data_x    : contains our dataset
+    :param data_y    : contains the output (result vector)
+    :param len_data  : len of the dataset
+    :param theta     : contains the feature vector
+    :return          : sum of square error computed from given feature's
+    """
+    error = 0.0
+    prod = np.dot(theta, data_x.transpose())
+    prod -= data_y.transpose()
+    sum_elem = np.sum(np.square(prod))
+    error = sum_elem / (2 * len_data)
+    return error
+
+
+def run_linear_regression(data_x, data_y):
+    """ Implement Linear regression over the dataset
+    :param data_x  : contains our dataset
+    :param data_y  : contains the output (result vector)
+    :return        : feature for line of best fit (Feature vector)
+    """
+    iterations = 100000
+    alpha = 0.0001550
+
+    no_features = data_x.shape[1]
+    len_data = data_x.shape[0] - 1
+
+    theta = np.zeros((1, no_features))
+
+    for i in range(0, iterations):
+        theta = run_steep_gradient_descent(data_x, data_y,
+                                           len_data, alpha, theta)
+        error = sum_of_square_error(data_x, data_y, len_data, theta)
+        print('At Iteration %d - Error is %.5f ' % (i + 1, error))
+
+    return theta
+
+
+def main():
+    """ Driver function """
+    data = collect_dataset()
+
+    len_data = data.shape[0]
+    data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float)
+    data_y = data[:, -1].astype(float)
+
+    theta = run_linear_regression(data_x, data_y)
+    len_result = theta.shape[1]
+    print('Resultant Feature vector : ')
+    for i in range(0, len_result):
+        print('%.5f' % (theta[0, i]))
+
+
+if __name__ == '__main__':
+    main()