From a356800824d55a8d507f767718a4dd25ee40f23b Mon Sep 17 00:00:00 2001
From: aslesha <cb.en.u4cse16259@cb.students.amrita.edu>
Date: Wed, 2 Jan 2019 12:21:27 +0530
Subject: [PATCH] Delete q2__16259.py
---
lab4/q2__16259.py | 122 ----------------------------------------------
1 file changed, 122 deletions(-)
delete mode 100644 lab4/q2__16259.py
diff --git a/lab4/q2__16259.py b/lab4/q2__16259.py
deleted file mode 100644
index 4e8bfb4..0000000
--- a/lab4/q2__16259.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import csv
-import numpy as np
-import matplotlib.pyplot as plt
-def loadCSV(filename):
- '''
- function to load dataset
- '''
- with open(filename,"r") as csvfile:
- lines = csv.reader(csvfile)
- dataset = list(lines)
- for i in range(len(dataset)):
- dataset[i] = [float(x) for x in dataset[i]]
- return np.array(dataset)
-def normalize(X):
- '''
- function to normalize feature matrix, X
- '''
- mins = np.min(X, axis = 0)
- maxs = np.max(X, axis = 0)
- rng = maxs - mins
- norm_X = 1 - ((maxs - X)/rng)
- return norm_X
-def logistic_func(beta, X):
- '''
- logistic(sigmoid) function
- '''
- return 1.0/(1 + np.exp(-np.dot(X, beta.T)))
-def log_gradient(beta, X, y):
- '''
- logistic gradient function
- '''
- first_calc = logistic_func(beta, X) - y.reshape(X.shape[0], -1)
- final_calc = np.dot(first_calc.T, X)
- return final_calc
-def cost_func(beta, X, y):
- '''
- cost function, J
- '''
- log_func_v = logistic_func(beta, X)
- y = np.squeeze(y)
- step1 = y * np.log(log_func_v)
- step2 = (1 - y) * np.log(1 - log_func_v)
- final = -step1 - step2
- return np.mean(final)
-
-def grad_desc(X, y, beta, lr=.01, converge_change=.001):
- '''
- gradient descent function
- '''
- cost = cost_func(beta, X, y)
- change_cost = 1
- num_iter = 1
-
- while(change_cost > converge_change):
- old_cost = cost
- beta = beta - (lr * log_gradient(beta, X, y))
- cost = cost_func(beta, X, y)
- change_cost = old_cost - cost
- num_iter += 1
-
- return beta, num_iter
-def pred_values(beta, X):
- '''
- function to predict labels
- '''
- pred_prob = logistic_func(beta, X)
- pred_value = np.where(pred_prob >= .5, 1, 0)
- return np.squeeze(pred_value)
-def plot_reg(X, y, beta):
- '''
- function to plot decision boundary
- '''
- # labelled observations
- x_0 = X[np.where(y == 0.0)]
- x_1 = X[np.where(y == 1.0)]
-
- # plotting points with diff color for diff label
- plt.scatter([x_0[:, 1]], [x_0[:, 2]], c='b', label='y = 0')
- plt.scatter([x_1[:, 1]], [x_1[:, 2]], c='r', label='y = 1')
-
- # plotting decision boundary
- x1 = np.arange(0, 1, 0.1)
- x2 = -(beta[0,0] + beta[0,1]*x1)/beta[0,2]
- plt.plot(x1, x2, c='k', label='reg line')
-
- plt.xlabel('x1')
- plt.ylabel('x2')
- plt.legend()
- plt.show()
-if __name__ == "__main__":
- # load the dataset
- dataset = loadCSV('dataset1.csv')
-
- # normalizing feature matrix
- X = normalize(dataset[:, :-1])
-
- # stacking columns wth all ones in feature matrix
- X = np.hstack((np.matrix(np.ones(X.shape[0])).T, X))
-
- # response vector
- y = dataset[:, -1]
-
- # initial beta values
- beta = np.matrix(np.zeros(X.shape[1]))
-
- # beta values after running gradient descent
- beta, num_iter = grad_desc(X, y, beta)
-
- # estimated beta values and number of iterations
- print("Estimated regression coefficients:", beta)
- print("No. of iterations:", num_iter)
-
- # predicted labels
- y_pred = pred_values(beta, X)
-
- # number of correctly predicted labels
- print("Correctly predicted labels:", np.sum(y == y_pred))
-
- # plotting regression line
- plot_reg(X, y, beta)
-
-
--
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