# General libraries
import os
import numpy as np
# Other functions
from warmUpExercise import *
from plotData import *
from computeCost import *
from gradientDescent import *
from predic import *
## ======== Part 1: Basic Function ======== ##
# Complete warmUpExercise
print('Runing warmUpExercise...\n')
print('5x5 Identity Matrix: \n')
print(warmUpExercise())
print('\n')
## ======== Part 2: Plotting ======== ##
f = open('../Data/ex1data1.txt')
content = f.readlines()
f.close()
X = []
y = []
m = len(content)
for line in content:
line = line.replace('\n', '')
x_, y_ = line.split(',')
X.append(float(x_))
y.append(float(y_))
from warmUpExercise import *
from featureNormalize import *
from normalEqn import *
import os
os.chdir('/home/morena/MachineLearning/AndrewNg_Python/2.LinearRegression/machine-learning-ex1/ex1')
# Inputs to the functions
X1 = np.transpose(
np.array([np.ones(20), np.exp(1) + np.exp(2) * np.arange(0.1, 2.1, 0.1)]))
Y1 = np.transpose(np.array([X1[:, 1]+np.sin(X1[:, 0])+np.cos(X1[:, 1])]))
X2 = np.transpose(np.array([X1[:, 1]**0.5, X1[:, 1]**0.25]))
X2 = np.concatenate((X1, X2), axis=1)
Y2 = np.array(Y1**0.5 + Y1)
# WarmUpExercise
print('Print WarmUpExercise:\n{}'.format(warmUpExercise(5)))
# computeCost with one variable
print('Print computeCost with one variable:\n{}'.format(computeCost(X1, Y1.transpose(),
np.array([0.5, -0.5]).transpose())))
# gradientDescent with one variable
(theta, J_history) = gradientDescent(
X1, Y1[:, 0], np.array([0.5, -0.5]).transpose(), 0.01, 10)
print('theta_single = {}, J_history_single = {}'.format(theta, J_history))
# Feature Normalization
[X_norm, mu, sigma] = featureNormalize(X2[:, 1:3])
print('X_norm:\n{}\nmu:{}\nsigma{}'.format(X_norm, mu, sigma))
# computeCost with multiple variables
from mpl_toolkits.mplot3d import Axes3D
from warmUpExercise import *
from plotData import *
from computeCost import *
from gradientDescent import *
def pause():
programPause = input('Program paused. Press enter to continue.')
## ==================== Part 1: Basic Function ====================
# refer to warmUpExercise.py
print('Running warmUpExercise ... ')
Matrix = warmUpExercise()
print('5x5 Identity Matrix: ')
print(Matrix)
pause()
## ======================= Part 2: Plotting =======================
print('Plotting Data ...')
data = loadtxt('ex1data1.txt', delimiter=',')
X = data[:, 0]
y = data[:, 1]
m = len(y) # number of training examples
# Plot Data
# Note: refer to plotData.py
plotData(X, y)
# Machine Learning Online Class - Exercise 1: Linear Regression
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from gradientDescent import *
from computeCost import *
from plotData import *
from warmUpExercise import *
# ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.py
print('\nRunning warmUpExercise ... \n')
print('5x5 Identity Matrix: \n')
warmUpExercise(5)
input('\nProgram paused. Press Enter to continue.\n')
# ======================= Part 2: Plotting =======================
print('Plotting Data ...\n')
data = pd.read_csv('ex1data1.txt', header=None)
X = np.array(data[0])
y = np.array(data[1])
m = len(y) # number of training examples
# Plot Data
plotData(X, y)
def testWarmUp(self): '''trying to compare arrays but see http://stackoverflow.com/questions/1322380/gotchas-where-numpy-differs-from-straight-python and http://stackoverflow.com/questions/3302949/whats-the-best-way-to-assert-for-scipy-array-equality''' assert_array_equal(warmUpExercise(), array([])) self.assertTrue((warmUpExercise() == array([])).all())
from bokeh.plotting import figure, output_notebook, show, output_file
from decimal import *
# Importing own modules
import warmUpExercise
import plotData
import computeCost
# ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.py
print('Running warmUpExercise function ....\n')
print('5x5 Identity Matrix: \n')
# Calling warmUpExercise() from file warmUpExercise.py
print warmUpExercise(5)
# Script is paused. User should press Enter in order to continue
raw_input('Program paused. Press <Enter> to continue.\n')
# ======================= Part 2: Plotting =======================
print('Plotting Data ...\n')
# Loading data from a textfile
data = np.loadtxt('../DATA/ex1data1.txt', delimiter=",")
# Assigning different columns of data to variables X and y
# X --> the population of a city and the second column is
# y --> the prfit of a food truck in that city
X = data[:, 0]
#
import pdb
## Initialization
from warmUpExercise import *
from plotData import *
from computeCost import *
from gradientDescent import *
from mpl_toolkits.mplot3d import axes3d, Axes3D
# is there any equivalent to "clear all; close all; clc"?
## ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.py
print 'Running warmUpExercise ... '
print '5x5 Identity Matrix: '
print warmUpExercise()
print('Program paused. Press enter to continue.')
raw_input()
## ======================= Part 2: Plotting =======================
print 'Plotting Data ...'
data = loadtxt('ex1data1.txt')
X = data[:, 0]; y = data[:, 1]
m = len(y) # number of training examples
# Plot Data
# Note: You have to complete the code in plotData.m
firstPlot = plotData(X, y)
firstPlot.show()