#!/usr/bin/env python

# coding: utf-8

# In[30]:

from download_data import download_data

import numpy as np

import matplotlib.pyplot as plt

from GD import gradientDescent

#from dataNormalization import rescaleMatrix1

# Starting codes for ha3 of CS596.

#NOTICE: Fill in the codes between "%PLACEHOLDER#start" and "PLACEHOLDER#end"

# There are two PLACEHODERS IN THIS SCRIPT

# parameters

################PLACEHOLDER1 #start##########################

# test multiple learning rates and report their convergence curves.

ALPHA = 0.03

MAX_ITER = 750

################PLACEHOLDER1 #end##########################

#% step-1: load data and divide it into two subsets, used for training and testing

sat = download_data('sat.csv', [1, 2, 4]).values # three columns: MATH SAT, VERB SAT, UNI. GPA # convert frame to matrix

################PLACEHOLDER2 #start##########################

# Normalize data

def rescaleMatrix1(dataMatrix):

return newMatrix

sat = rescaleMatrix1(sat)

################PLACEHOLDER2 #end##########################

# training data;

satTrain = sat[0:60, :]

# testing data;

satTest = sat[60:len(sat),:]

#% step-2: train a linear regression model using the Gradient Descent (GD) method

# ** theta and xValues have 3 columns since have 2 features: y = (theta * x^0) + (theta * x^1) + (theta * x^2)

theta = np.zeros(3)

xValues = np.ones((60, 3))

xValues[:, 1:3] = satTrain[:, 0:2]

yValues = satTrain[:, 2]

# call the GD algorithm, placeholders in the function gradientDescent()

[theta, arrCost] = gradientDescent(xValues, yValues, theta, ALPHA, MAX_ITER)

#visualize the convergence curve

plt.plot(range(0,len(arrCost)),arrCost);

plt.xlabel('iteration')

plt.ylabel('cost')

plt.title('alpha = {} theta = {}'.format(ALPHA, theta))

plt.show()

#% step-3: testing

testXValues = np.ones((len(satTest), 3))

testXValues[:, 1:3] = satTest[:, 0:2]

tVal = testXValues.dot(theta)

#% step-4: evaluation

# calculate average error and standard deviation

tError = np.sqrt([x**2 for x in np.subtract(tVal, satTest[:, 2])])

print('results: {} ({})'.format(np.mean(tError), np.std(tError)))

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