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Slow mlclass #1

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957750a
Fixed Ex1.py
Oct 5, 2014
d8e2edc
Fixed method predict and let ex2.py print the probability of the
physhik Oct 1, 2016
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12 changes: 7 additions & 5 deletions ex1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
from matplotlib import pyplot, cm
from mpl_toolkits.mplot3d import Axes3D

EX_DIRECTORY_PATH = '/Users/saburookita/Personal Projects/MachineLearningPy/'

def part1():
A = eye(5)
Expand Down Expand Up @@ -91,7 +92,7 @@ def plot(X, y):


def part2_1():
data = genfromtxt( "/Users/saburookita/Downloads/mlclass-ex1-004/mlclass-ex1/ex1data1.txt", delimiter=',')
data = genfromtxt( EX_DIRECTORY_PATH + "ex1data1.txt", delimiter=',')
X, y = data[:, 0], data[:, 1]
m = len(y)
y = y.reshape(m, 1)
Expand All @@ -101,7 +102,7 @@ def part2_1():


def part2_2():
data = genfromtxt( '/Users/saburookita/Downloads/mlclass-ex1-004/mlclass-ex1/ex1data1.txt', delimiter=',')
data = genfromtxt( EX_DIRECTORY_PATH + 'ex1data1.txt', delimiter=',')
X, y = data[:, 0], data[:, 1]
m = len(y)
y = y.reshape(m, 1)
Expand All @@ -127,7 +128,7 @@ def part2_2():


def part2_4():
data = genfromtxt('/Users/saburookita/Downloads/mlclass-ex1-004/mlclass-ex1/ex1data1.txt', delimiter=',')
data = genfromtxt( EX_DIRECTORY_PATH + "/ex1data1.txt", delimiter=',')
X, y = data[:, 0], data[:, 1]
m = len(y)
y = y.reshape(m, 1)
Expand All @@ -150,8 +151,9 @@ def part2_4():
pyplot.show(block=True)

fig = pyplot.figure()
ax = fig.gca(projection='3d')
pyplot.contourf(R, P, J_vals, logspace(-2, 3, 20))
# ax = fig.gca(projection='3d')
pyplot.contourf(R, P, J_vals.T, logspace(-2, 3, 20))
pyplot.plot(theta[0], theta[1], 'rx', markersize = 10)
pyplot.show(block=True)


Expand Down
151 changes: 81 additions & 70 deletions ex2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,91 +8,102 @@
from mpl_toolkits.mplot3d import Axes3D

def plot( data ):
positives = data[data[:,2] == 1]
negatives = data[data[:,2] == 0]
pyplot.xlabel("Exam 1 score")
pyplot.ylabel("Exam 2 score")
pyplot.xlim([25, 115])
pyplot.ylim([25, 115])

pyplot.scatter( negatives[:, 0], negatives[:, 1], c='y', marker='o', s=40, linewidths=1, label="Not admitted" )
pyplot.scatter( positives[:, 0], positives[:, 1], c='b', marker='+', s=40, linewidths=2, label="Admitted" )
pyplot.legend()
positives = data[data[:,2] == 1]
negatives = data[data[:,2] == 0]
pyplot.xlabel("Exam 1 score")
pyplot.ylabel("Exam 2 score")
pyplot.xlim([25, 115])
pyplot.ylim([25, 115])
pyplot.scatter( negatives[:, 0], negatives[:, 1], c='y', marker='o', s=40, linewidths=1, label="Not admitted" )
pyplot.scatter( positives[:, 0], positives[:, 1], c='b', marker='+', s=40, linewidths=2, label="Admitted" )
pyplot.legend()

def plotBoundary( data, X, theta ):
plot( data )
plot_x = array( [min(X[:,1]), max(X[:,1])] )
plot_y = (-1./ theta[2]) * (theta[1] * plot_x + theta[0])
pyplot.plot( plot_x, plot_y )
plot( data )
plot_x = array( [min(X[:,1]), max(X[:,1])] )
plot_y = (-1./ theta[2]) * (theta[1] * plot_x + theta[0])
pyplot.plot( plot_x, plot_y )

def sigmoid( z ):
return scipy.special.expit(z)
# return 1.0 / (1.0 + exp( -z ))
return scipy.special.expit(z)
# return 1.0 / (1.0 + exp( -z ))

def computeCost( theta, X, y ):
m = shape( X )[0]
hypo = sigmoid(X.dot( theta ))
term1 = log( hypo ).T.dot( -y )
term2 = log( 1.0 - hypo ).T.dot( 1-y )
return ((term1 - term2) / m).flatten()
m = shape( X )[0]
hypo = sigmoid(X.dot( theta ))
term1 = log( hypo ).T.dot( -y )
term2 = log( 1.0 - hypo ).T.dot( 1-y )
return ((term1 - term2) / m).flatten()

def gradientCost( theta, X, y ):
m = shape(X)[0]
return ( X.T.dot(sigmoid( X.dot( theta ) ) - y) ) / m
m = shape(X)[0]
return ( X.T.dot(sigmoid( X.dot( theta ) ) - y) ) / m

def costFunction( theta, X, y ):
cost = computeCost( theta, X, y )
gradient = gradientCost( theta, X, y )
# m = shape( X )[0]
# hypo = sigmoid( X.dot( theta ) )
# term1 = log( hypo ).transpose().dot( -y )
# term2 = log( 1.0 - hypo ).transpose().dot( 1 - y )
# cost = ((term1 - term2) / m)
# gradient = (X.transpose().dot( hypo - y) ) / m
return cost

def findMinTheta( theta, X, y ):
result = scipy.optimize.fmin( costFunction, x0=theta, args=(X, y), maxiter=500, full_output=True )
return result[0], result[1]

def predict( theta, X, binary=True ):
prob = sigmoid( theta.dot( X ))
if binary :
return 1 if prob > 0.5 else 0
else:
return prob
cost = computeCost( theta, X, y )
gradient = gradientCost( theta, X, y )
# m = shape( X )[0]
# hypo = sigmoid( X.dot( theta ) )
# term1 = log( hypo ).transpose().dot( -y )
# term2 = log( 1.0 - hypo ).transpose().dot( 1 - y )
# cost = ((term1 - term2) / m)
# gradient = (X.transpose().dot( hypo - y) ) / m
return cost

def findMinTheta( theta, X, y ): #Find theta which gives minimum cost at theta
result = scipy.optimize.fmin( costFunction, x0=theta, args=(X, y), maxiter=500, full_output=True )
return result[0], result[1] # (theta, cost)

def predict( theta, X ):
p=[]
prob = sigmoid( X.dot(theta) )
for i in range(len(X)):
if prob[i] > 0.5 :
p.append([1])
else:
p.append([0])
return p


def part1_1():
data = genfromtxt( "/Users/saburookita/Downloads/mlclass-ex2-004/mlclass-ex2/ex2data1.txt", delimiter = ',' )
m, n = shape( data )[0], shape(data)[1] - 1
X = c_[ ones((m, 1)), data[:, :n] ]
y = data[:, n:n+1]

plot( data )
pyplot.show()

filename = "~/.../ex2data1.txt"
data = genfromtxt( filename, delimiter = ',' )
m, n = shape( data )[0], shape(data)[1] - 1
X = c_[ ones((m, 1)), data[:, :n] ]
y = data[:, n:n+1]

plot( data )
pyplot.show()

def part1_2():
data = genfromtxt( "/Users/saburookita/Downloads/mlclass-ex2-004/mlclass-ex2/ex2data1.txt", delimiter = ',' )
m, n = shape( data )[0], shape(data)[1] - 1
X = c_[ ones((m, 1)), data[:, :n] ]
y = data[:, n:n+1]
theta = zeros( (n+1, 1) )
filename = "~/.../ex2data1.txt"
data = genfromtxt( filename, delimiter = ',' )
m, n = shape( data )[0], shape(data)[1] - 1
X = c_[ ones((m, 1)), data[:, :n] ]
y = data[:, n:n+1]
theta = zeros( (n+1, 1) )

print "Cost at initial theta (zeros):", computeCost(theta, X, y)[0]
print "Gradient at initial theta (zeros):\n", gradientCost(theta, X, y)

theta, cost = findMinTheta( theta, X, y )
print "Cost at theta found by fminunc:", cost
plotBoundary( data, X, theta )
pyplot.show()

prob = sigmoid(array([1, 45, 85]).dot(theta))
print 'For a student with scores 45 and 85, we predict an admission probability of ' , prob
print "The train accuracy is:" , mean(predict(theta,X)==y)

print computeCost(theta, X, y)
theta, cost = findMinTheta( theta, X, y )
plotBoundary( data, X, theta )
pyplot.show()

test = array([1, 45, 85])
print predict( test, theta )

def main():
set_printoptions(precision=6, linewidth=200)
part1_1()
part1_2()
set_printoptions(precision=6, linewidth=200)
part1_1()
part1_2()


if __name__ == '__main__':
main()
main()