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#13 Column names added #20

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#13 Column names added #20

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15 changes: 9 additions & 6 deletions demo.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,8 @@
#The optimal values of m and b can be actually calculated with way less effort than doing a linear regression.
#this is just to demonstrate gradient descent

from numpy import *

import numpy as np
import pandas as pd
# y = mx + b
# m is slope, b is y-intercept
def compute_error_for_line_given_points(b, m, points):
Expand Down Expand Up @@ -34,15 +34,18 @@ def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_i
return [b, m]

def run():
points = genfromtxt("data.csv", delimiter=",")
points = np.genfromtxt("data.csv", delimiter=",")
arr=np.array(['0','1'])
points=arr.append(points)
points.rename(columns={'0':'column1','1':'column2'})
learning_rate = 0.0001
initial_b = 0 # initial y-intercept guess
initial_m = 0 # initial slope guess
num_iterations = 1000
print "Starting gradient descent at b = {0}, m = {1}, error = {2}".format(initial_b, initial_m, compute_error_for_line_given_points(initial_b, initial_m, points))
print "Running..."
print("Starting gradient descent at b = {0}, m = {1}, error = {2}".format(initial_b, initial_m, compute_error_for_line_given_points(initial_b, initial_m, points)))
print("Running...")
[b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)
print "After {0} iterations b = {1}, m = {2}, error = {3}".format(num_iterations, b, m, compute_error_for_line_given_points(b, m, points))
print("After {0} iterations b = {1}, m = {2}, error = {3}".format(num_iterations, b, m, compute_error_for_line_given_points(b, m, points)))

if __name__ == '__main__':
run()