def output(partId):
# Random Test Cases
X1 = np.column_stack(
(np.ones(20), np.exp(1) + np.exp(2) * np.linspace(0.1, 2, 20)))
Y1 = X1[:, 1] + np.sin(X1[:, 0]) + np.cos(X1[:, 1])
X2 = np.column_stack((X1, X1[:, 1]**0.5, X1[:, 1]**0.25))
Y2 = np.power(Y1, 0.5) + Y1
if partId == '1':
out = formatter('%0.5f ', warmUpExercise())
elif partId == '2':
out = formatter('%0.5f ', computeCost(X1, Y1, np.array([0.5, -0.5])))
elif partId == '3':
out = formatter(
'%0.5f ', gradientDescent(X1, Y1, np.array([0.5, -0.5]), 0.01, 10))
elif partId == '4':
out = formatter('%0.5f ', featureNormalize(X2[:, 1:4]))
elif partId == '5':
out = formatter(
'%0.5f ', computeCostMulti(X2, Y2, np.array([0.1, 0.2, 0.3, 0.4])))
elif partId == '6':
out = formatter(
'%0.5f ',
gradientDescentMulti(X2, Y2, np.array([-0.1, -0.2, -0.3, -0.4]),
0.01, 10))
elif partId == '7':
out = formatter('%0.5f ', normalEqn(X2, Y2))
return out
def output(partId):
# Random Test Cases
X1 = column_stack((ones(20), exp(1) + dot(exp(2), arange(0.1, 2.1, 0.1))))
Y1 = X1[:,1] + sin(X1[:,0]) + cos(X1[:,1])
X2 = column_stack((X1, X1[:,1]**0.5, X1[:,1]**0.25))
Y2 = Y1**0.5 + Y1
if partId == '1':
return sprintf('%0.5f ', warmUpExercise())
elif partId == '2':
return sprintf('%0.5f ', computeCost(X1, Y1, array([0.5, -0.5])))
elif partId == '3':
return sprintf('%0.5f ', gradientDescent(X1, Y1, array([0.5, -0.5]), 0.01, 10))
elif partId == '4':
return sprintf('%0.5f ', featureNormalize(X2[:,1:3]));
elif partId == '5':
return sprintf('%0.5f ', computeCostMulti(X2, Y2, array([0.1, 0.2, 0.3, 0.4])))
elif partId == '6':
return sprintf('%0.5f ', gradientDescentMulti(X2, Y2, array([-0.1, -0.2, -0.3, -0.4]), 0.01, 10))
elif partId == '7':
return sprintf('%0.5f ', normalEqn(X2, Y2))
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
# x refers to the population size in 10,000s
# y refers to the profit in $10,000s
#
## Initialization
##clear # close all# clc
## ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.m
print('Running warmUpExercise ... \n')#
print('5x5 Identity Matrix: \n')#
print(warmUpExercise())
print('Program paused. Press enter to continue.\n')#
raw_input(">>>")
#
#
# ## ======================= Part 2: Plotting =======================
print('Plotting Data ...\n')
data = np.loadtxt('ex1data1.txt',delimiter=',')#
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
import pylab as pl
#import 3d axes plots from matpotlib
from mpl_toolkits.mplot3d import Axes3D
#importing plotData file with the function for ploting the curves and we access it use initials plt
import plotData as plt
#importing gradientDescent as gD
import gradientDescent as gD
#importing warmUpExercise file with identity matrix function
import warmUpExercise as wmUpEx
#import computeCost file with computeCost function and to access it we use initials as cC
import computeCost as cC
print("Running warmUpExercise ... \n")
print("5x5 Identity Matrix: \n")
#warmUpExercise to print the identity matrix similar to eye(5) in octaveor matlab
A=wmUpEx.warmUpExercise()
print(A)#printing the identity matrix
print("Program paused press enter to continue ")
"""%% ======================= Part 2: Plotting ======================= """
#load data located in ex1data1 in a variable called data
data=np.loadtxt('ex1data1.txt',delimiter=",")
#load data in the first column of variable data in variable X
#unlike in matlab in python indexing starts at 0
X = data[:, 0]
#load data in second column of variable data in variable y
y = data[:, 1]
#get the length of y
m=len(y)
def testWarmUp():
assert_array_equal(warmUpExercise(), eye(5))
import warmUpExercise as wue
import plotData as pd
import computeCost as cc
import featureNormalize as fn
import gradientDescent as gd
import normalEq as ne
import numpy as np
import matplotlib.pyplot as plt
print "Running warmUpExercise"
print "5*5 identity matrix"
wue.warmUpExercise()
raw_input("Press Enter to continue.....")
print "Plotting Data"
pd.plotData()
raw_input("Press Enter to continue.....")
a=pd.content[0]
X=np.ones((20,2))
X[:,1]=a
b=pd.content[1]
y=np.ones((20,1))
y[:,0]=b
theta=np.zeros((2,1))
iterations=100
alpha= 0.01
J=cc.computeCost(X,y,theta)
print "Computed Cost: ",J
import numpy as np
from plotData import plotData
from computeCost import computeCost
from gradientDescent import gradientDescent
from warmUpExercise import warmUpExercise
import matplotlib.pyplot as plt
def pause():
input("Press the <ENTER> key to continue...")
"""## Part 1: Basic Function """
print("Running warmUpExercise ... \n")
print("5x5 Identity Matrix: \n")
print(warmUpExercise())
print("Program paused. Press enter to continue.\n")
pause()
"""## ======================= Part 2: Plotting """
print("Plotting Data ...\n")
data = np.loadtxt('ex1data1.txt', delimiter =",")
X = data[:, 0] #x refers to the population size in 10,000s
y = data[:, 1] #y refers to the profit in $10,000s
m = y.size #umber of training examples
y = y.reshape((m,1))
# import pandas as pd
import numpy as np
# clear close all clc
## ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.py
# Readings: https://docs.python.org/3/tutorial/modules.html
from warmUpExercise import warmUpExercise
print('Running warmUpExercise ... \n')
print('5x5 Identity Matrix: \n')
warmUpExercise()
input('Program paused. Press enter to continue.\n')
## ======================= Part 2: Plotting =======================
print('Plotting Data ...\n')
# Readings: https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html
# https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.copy.html
DATA_PATH = "ex1data1.txt"
data = np.loadtxt(DATA_PATH, delimiter=',')
# Used np.copy here because the original data should be unchanged in case its needed later
X = np.copy(data[:, 0])
from matplotlib import cm
import pandas as pd
import numpy as np
from numpy import genfromtxt
import warmUpExercise
import computeCost
import gradientDescent
#import plotData as plotData;
np.set_printoptions(precision=2)
## ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.m
print('Running warmUpExercise ... \n')
print('5x5 Identity Matrix: \n')
A = warmUpExercise.warmUpExercise()
print(A)
input("Press the <ENTER> key to continue...")
##======================= Part 2: Plotting =======================
print('Plotting Data ...\n')
my_data = genfromtxt(
"E:\Private\ML\machine-learning-ex1-python\ex1\ex1data1.txt",
delimiter=',')
data = pd.read_csv(
'E:\Private\ML\machine-learning-ex1-python\ex1\ex1data1.txt')
X = my_data[:, 0]
y = my_data[:, 1]
m = y.shape
# Plot Data
def testWarmUp():
assert_array_equal(warmUpExercise(), eye(5))
#
# INITIALIZE
import os
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
os.system('cls' if os.name == 'nt' else 'clear')
plt.close("all")
## =================== Part 1: Basic Function ===================
from warmUpExercise import warmUpExercise
print('A basic function.')
print(' - A 5x5 Identity Matrix: \n')
# FILE: warmUpExercise.py
print(warmUpExercise(), '\n')
input('Paused. Press enter to continue.\n')
## =================== Part 2: Plotting ====================
from plotData import plotData
# x= population size in 10,000s
# y= profit in $10,000s
print('Plotting data')
data = pd.read_csv('ex1data1.txt', names=['Population', 'Profit'])
X = data.iloc[:, 0]
y = data.iloc[:, 1]
m = data.shape[0] #tuple (row, col) // looking for row
#FILE: plotData.py
plotData(X, y)
input('Paused. Press enter to continue.\n')
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import warmUpExercise as warmEx
import numpy as np
import matplotlib.pyplot as plt
import gradientDescent as gd
from matplotlib import cm
print "Running warmUpExercise ... "
print "5x5 Identity Matrix:"
warmEx.warmUpExercise()
try:
input('Program paused. Press enter to continue...')
except SyntaxError:
pass
print 'Plotting Data ...'
""" ======================= Part 2: Plotting ======================= """
data = np.genfromtxt('ex1data1.txt',delimiter=',')
#print data
X = data[:,0]
y = data[:,1]
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from warmUpExercise import warmUpExercise
from plotData import plotData, plotData2
from gradientDescent import gradientDescent
from computeCost import computeCost
# ==================== Part 1: Basic Function ====================
print('Running warmup exercise ...')
print('5x5 Identity Matrix:')
print(warmUpExercise(), '\n')
# ======================= Part 2: Plotting =======================
print('Plotting Data ...\n')
data = np.loadtxt('ex1data1.txt', delimiter=',')
X, y = data[:, 0], data[:, 1]
plotData(X, y)
# =================== Part 3: Gradient descent ===================
print('Running Gradient Descent ...')
m, n = data.shape
X = np.hstack((np.ones((m, 1)), data[:, [0]]))
y = data[:, [1]]
theta = np.zeros((2, 1))
iterations = 1500
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import warmUpExercise as wue
import plotData as pd
import computeCost as cc
import gradientDescent as gd
## ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.py
print('Running warmUpExercise...')
print('5x5 Identity Matrix: ')
print(wue.warmUpExercise())
raw_input('Program paused. Press enter to continue.\n')
## ======================= Part 2: Plotting =======================
print('Plotting Data...')
data = np.loadtxt('ex1data1.txt', delimiter=",")
X = data[:,0]
y = data[:,1]
m = len(y) # number of training examples
# Plot Data
# Note: You have to complete the code in plotData.py
pd.plotData(X, y)
from __future__ import division
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#from matplotlib import cm
import numpy as np
import warmUpExercise, computeCost, gradientDescent
print "Running warmUpExercise"
print "5x5 Indentity Matrix:"
print warmUpExercise.warmUpExercise()
print "Program paused. Press enter to continue."
raw_input("Press ENTER to continue")
print ("Plotting data")
#infile = open('ex1data1.txt', 'r')
#for line in infile:
# data.append(line)
#infile.close()
data = np.loadtxt('ex1data1.txt', delimiter = ',')
X = data[:,0]
y = np.matrix(data[:,1]).T
m = len(y)
plt.figure()
plt.plot(X, y, 'o')
#plt.savefig('temp.png')
plt.show()
def Matrix(self):
matrix = warmUpExercise.warmUpExercise()
return matrix
# gradientDescentMulti.py
# computeCostMulti.py
# featureNormalize.py
# normalEqn.py
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
# x refers to the population size in 10,000s
# y refers to the profit in $10,000s
# ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.py
print 'Running warmUpExercise ...'
print '5x5 Identity Matrix:'
warmup = warmUpExercise()
print warmup
#raw_input("Program paused. Press Enter to continue...")
# ======================= Part 2: Plotting =======================
data = np.loadtxt('C:\Users\HTDA\Coursera-Stanford-ML-Python\ex1\ex1data1.txt', delimiter=',')
m = data.shape[0]
X = np.vstack(zip(np.ones(m),data[:,0]))
y = data[:, 1]
# Plot Data
# Note: You have to complete the code in plotData.py
print 'Plotting Data ...'
plotData(data)
#show()
# featureNormalize.py
# normalEqn.py
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
# x refers to the population size in 10,000s
# y refers to the profit in $10,000s
#
#
# Initialization
#
# ==================== Part 1: Basic Function ====================
print('Running warmUpExercise ... \n')
print('5x5 Identity Matrix: \n')
print(WUE.warmUpExercise())
input("Press Enter to continue...")
data = pd.read_csv('ex1data2.txt', header=None)
X, y = data.iloc[:, :2], data.iloc[:,
2] #Data Separated into two pandas series
m = np.size(y) #number of training example
#Print out some data points
print('First 10 examples from the dataset: \n')
print('Printing X\n', X.head(10))
print('Printing y\n', y.head(10))
# Scale features and set them to zero mean
print('Normalizing Features ...\n')
[X, mu, sigma] = FN.featureNormalize(X)
# Add intercept term to X
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import warmUpExercise as wue
import plotData as pd
import computeCost as cc
import gradientDescent as gd
## ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.py
print('Running warmUpExercise...')
print('5x5 Identity Matrix: ')
print(wue.warmUpExercise())
raw_input('Program paused. Press enter to continue.\n')
## ======================= Part 2: Plotting =======================
print('Plotting Data...')
data = np.loadtxt('ex1data1.txt', delimiter=",")
X = data[:, 0]
y = data[:, 1]
m = len(y) # number of training examples
# Plot Data
# Note: You have to complete the code in plotData.py
pd.plotData(X, y)
# featureNormalize.py
# normalEqn.py
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
# x refers to the population size in 10,000s
# y refers to the profit in $10,000s
#
## ==================== Part 1: Basic Function ====================
from warmUpExercise import warmUpExercise
print pcolor.WARN+"Runing warmUpExercise.py..."+pcolor.ENDC
print pcolor.NOTE+"5x5 Indentity Matrix:"+pcolor.ENDC
warmUpExercise()
raw_input(pcolor.WARN+"Program paused.Press enter key to continue..."+pcolor.ENDC)
## ======================= Part 2: Plotting =======================
print "Plotting Data..."
f = open('ex1data1.txt')
X = np.array(np.empty(1))
y = np.array(np.empty(1))
for tmp in f.readlines():
tmp = tmp.split(',')
X = np.append(X,float(tmp[0].split()[0]))
y = np.append(y,float(tmp[1].split()[0]))
from plotData import plotDisData as plotd
plotd(X,y)
raw_input("Program paused.Press enter key to continue...")
# gradientDescentMulti.py
# computeCostMulti.py
# featureNormalize.py
# normalEqn.py
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
# x refers to the population size in 10,000s
# y refers to the profit in $10,000s
# ==================== Part 1: Basic Function ====================
# Complete warmUpExercise.py
print('Running warmUpExercise ...')
print('5x5 Identity Matrix:')
warmup = warmUpExercise()
print(warmup)
input("Program paused. Press Enter to continue...")
# ======================= Part 2: Plotting =======================
data = np.loadtxt('ex1data1.txt', delimiter=',')
m = data.shape[0]
X = np.vstack(zip(np.ones(m),data[:,0]))
y = data[:, 1]
# Plot Data
# Note: You have to complete the code in plotData.py
print('Plotting Data ...')
plotData(data)
plt.show()
from numpy import *
from matplotlib.pyplot import *
from mpl_toolkits.mplot3d import axes3d, Axes3D
from warmUpExercise import warmUpExercise
from plotData import plotData
from computeCost import computeCost
from gradientDescent import gradientDescent
# 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', delimiter=',')
X = data[:, 0]
y = data[:, 1]
m = len(y) # number of training examples
# Plot Data
# Note: You have to complete the code in plotData.py
firstPlot = plotData(X, y)
firstPlot.show()
