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linear_perceptron.py
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linear_perceptron.py
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import numpy as np
import pandas as pd
import datetime
import read_data
from prettytable import PrettyTable
import matplotlib.pyplot as pp
# Input: number of iterations L
# numpy matrix X of features, with n rows (samples), d columns (features)
# X[i,j] is the j-th feature of the i-th sample
# numpy vector y of labels, with n rows (samples), 1 column
# y[i] is the label (+1 or -1) of the i-th sample
# Output: numpy vector theta of d rows, 1 column
def train(L,X,y):
(n,d)=np.shape(X)
theta = np.zeros((d, 1))
for i in range(0, L):
X, y = shuffle_in_unison(X,y)
for t in range(0, n):
if (y[t] * (np.dot(X[t], theta))[0]) <= 0:
theta = theta + np.array([y[t]* X[t]]).T
return theta
def shuffle_in_unison(a,b):
assert len(a)==len(b)
shuffled_a = np.empty(a.shape, dtype=a.dtype)
shuffled_b = np.empty(b.shape, dtype=b.dtype)
permutation = np.random.permutation(len(a))
for old_index, new_index in enumerate(permutation):
shuffled_a[new_index] = a[old_index]
shuffled_b[new_index] = b[old_index]
return shuffled_a, shuffled_b
# Input: numpy vector theta of d rows, 1 column
# numpy vector x of d rows, 1 column
# Output: label (+1 or -1)
def linpred(theta,x):
return 1 if np.dot(theta,x) > 0 else -1
def run():
t = PrettyTable(['# of iterations', '# of mistakes', 'Date included?'])
iteration_number = 100
for a in range(0,2):
#Reading the data from the training set with all features included
features_matrix_all, labels_matrix_all = read_data.read_training_data_all()
#Running our linear perceptron on 10,000/100,000 iterations
theta = train(iteration_number, features_matrix_all, labels_matrix_all)
#Testing convergence by seeing if the perceptron will make any mistakes predicting on the same data it trained on
mistakes_count = 0
for i in range(0, len(features_matrix_all)):
xii = np.matrix(features_matrix_all[i])
ai = linpred(xii, theta)
bi = labels_matrix_all[i]
if(ai != bi):
mistakes_count+=1
t.add_row([iteration_number, mistakes_count, "Yes"])
for b in range(0,2):
#Reading the data from the training set with no date/time feature
features_matrix_no_time, labels_matrix_no_time = read_data.read_training_data_no_time()
#Running our linear perceptron with no date on 10,000/100,000 iterations
theta = train(iteration_number, features_matrix_no_time, labels_matrix_no_time)
#Testing convergence by seeing if the perceptron will make any mistakes predicting on the same data it trained on
mistakes_count = 0
for i in range(0, len(features_matrix_no_time)):
xii = np.matrix(features_matrix_no_time[i])
ai = linpred(xii, theta)
bi = labels_matrix_no_time[i]
if(ai != bi):
mistakes_count+=1
t.add_row([iteration_number, mistakes_count, "No"])
print t
if __name__=='__main__':
run()