def output(partId):
# Random Test Cases
X = np.stack([
np.ones(20),
np.exp(1) * np.sin(np.arange(1, 21)),
np.exp(0.5) * np.cos(np.arange(1, 21))
],
axis=1)
y = (np.sin(X[:, 0] + X[:, 1]) > 0).astype(float)
Xm = np.array([[-1, -1], [-1, -2], [-2, -1], [-2, -2], [1, 1], [1, 2],
[2, 1], [2, 2], [-1, 1], [-1, 2], [-2, 1], [-2, 2], [1, -1],
[1, -2], [-2, -1], [-2, -2]])
ym = np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3])
t1 = np.sin(np.reshape(np.arange(1, 25, 2), (4, 3), order='F'))
t2 = np.cos(np.reshape(np.arange(1, 41, 2), (4, 5), order='F'))
if partId == '1':
J, grad = lrCostFunction(np.array([0.25, 0.5, -0.5]), X, y, 0.1)
out = formatter('%0.5f ', J)
out += formatter('%0.5f ', grad)
elif partId == '2':
out = formatter('%0.5f ', oneVsAll(Xm, ym, 4, 0.1))
elif partId == '3':
out = formatter('%0.5f ', predictOneVsAll(t1, Xm))
elif partId == '4':
out = formatter('%0.5f ', predict(t1, t2, Xm))
return out
def output(partId):
# Random Test Cases
x1 = np.sin(np.arange(1, 11))
x2 = np.cos(np.arange(1, 11))
ec = 'the quick brown fox jumped over the lazy dog'
wi = np.abs(np.round(x1 * 1863)).astype(int)
wi = np.concatenate([wi, wi])
if partId == '1':
sim = gaussianKernel(x1, x2, 2)
out = formatter('%0.5f ', sim)
elif partId == '2':
mat = scipy.io.loadmat('ex6data3.mat')
X = mat['X']
y = mat['y'].ravel()
Xval = mat['Xval']
yval = mat['yval'].ravel()
C, sigma = dataset3Params(X, y, Xval, yval)
out = formatter('%0.5f ', C)
out += formatter('%0.5f ', sigma)
elif partId == '3':
word_indices = processEmail(ec) + 1
out = formatter('%d ', word_indices)
elif partId == '4':
x = emailFeatures(wi)
out = formatter('%d ', x)
return out
def output(partId):
# Random Test Cases
X = np.reshape(3 * np.sin(np.arange(1, 31)), (3, 10), order='F')
Xm = np.reshape(np.sin(np.arange(1, 33)), (16, 2), order='F') / 5
ym = 1 + np.arange(1, 17) % 4
t1 = np.sin(np.reshape(np.arange(1, 25, 2), (4, 3), order='F'))
t2 = np.cos(np.reshape(np.arange(1, 41, 2), (4, 5), order='F'))
t = np.concatenate([t1.ravel(), t2.ravel()], axis=0)
if partId == '1':
J, _ = nnCostFunction(t, 2, 4, 4, Xm, ym, 0)
out = formatter('%0.5f ', J)
elif partId == '2':
J, _ = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5)
out = formatter('%0.5f ', J)
elif partId == '3':
out = formatter('%0.5f ', sigmoidGradient(X))
elif partId == '4':
J, grad = nnCostFunction(t, 2, 4, 4, Xm, ym, 0)
out = formatter('%0.5f ', J)
out += formatter('%0.5f ', grad)
elif partId == '5':
J, grad = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5)
out = formatter('%0.5f ', J)
out += formatter('%0.5f ', grad)
return out
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
X = np.stack([
np.ones(20),
np.exp(1) * np.sin(np.arange(1, 21)),
np.exp(0.5) * np.cos(np.arange(1, 21))
],
axis=1)
y = np.reshape((np.sin(X[:, 0] + X[:, 1]) > 0).astype(float), (20, 1))
if partId == '1':
out = formatter('%0.5f ', sigmoid(X))
elif partId == '2':
out = formatter(
'%0.5f ',
costFunction(np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X,
y))
elif partId == '3':
cost, grad = costFunction(
np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X, y)
out = formatter('%0.5f ', grad)
elif partId == '4':
out = formatter(
'%0.5f ',
predict(np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X))
elif partId == '5':
out = formatter(
'%0.5f ',
costFunctionReg(np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X,
y, 0.1))
elif partId == '6':
cost, grad = costFunctionReg(
np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X, y, 0.1)
out = formatter('%0.5f ', grad)
return out
def output(partId):
# Random Test Cases
X = np.vstack([
np.ones(10),
np.sin(np.arange(1, 15, 1.5)),
np.cos(np.arange(1, 15, 1.5))
]).T
y = np.sin(np.arange(1, 31, 3))
Xval = np.vstack([
np.ones(10),
np.sin(np.arange(0, 14, 1.5)),
np.cos(np.arange(0, 14, 1.5))
]).T
yval = np.sin(np.arange(1, 11))
if partId == '1':
J, _ = linearRegCostFunction(X, y, np.array([0.1, 0.2, 0.3]), 0.5)
out = formatter('%0.5f ', J)
elif partId == '2':
J, grad = linearRegCostFunction(X, y, np.array([0.1, 0.2, 0.3]), 0.5)
out = formatter('%0.5f ', grad)
elif partId == '3':
error_train, error_val = learningCurve(X, y, Xval, yval, 1)
out = formatter(
'%0.5f ', np.concatenate([error_train.ravel(),
error_val.ravel()]))
elif partId == '4':
X_poly = polyFeatures(X[1, :].T, 8)
out = formatter('%0.5f ', X_poly)
elif partId == '5':
lambda_vec, error_train, error_val = validationCurve(X, y, Xval, yval)
out = formatter(
'%0.5f ',
np.concatenate(
[lambda_vec.ravel(),
error_train.ravel(),
error_val.ravel()]))
return out
def output(partId):
# Random Test Cases
X = np.sin(np.arange(1, 166)).reshape(15, 11, order='F')
Z = np.cos(np.arange(1, 122)).reshape(11, 11, order='F')
C = Z[:5, :]
idx = np.arange(1, 16) % 3
if partId == '1':
idx = findClosestCentroids(X, C) + 1
out = formatter('%0.5f ', idx.ravel('F'))
elif partId == '2':
centroids = computeCentroids(X, idx, 3)
out = formatter('%0.5f ', centroids.ravel('F'))
elif partId == '3':
U, S = pca(X)
out = formatter(
'%0.5f ', np.abs(np.hstack([U.ravel('F'),
np.diag(S).ravel('F')])))
elif partId == '4':
X_proj = projectData(X, Z, 5)
out = formatter('%0.5f ', X_proj.ravel('F'))
elif partId == '5':
X_rec = recoverData(X[:, :5], Z, 5)
out = formatter('%0.5f ', X_rec.ravel('F'))
return out
def output(partId):
# Random Test Cases
n_u = 3
n_m = 4
n = 5
X = np.sin(np.arange(1, 1 + n_m * n)).reshape(n_m, n, order='F')
Theta = np.cos(np.arange(1, 1 + n_u * n)).reshape(n_u, n, order='F')
Y = np.sin(np.arange(1, 1 + 2 * n_m * n_u, 2)).reshape(n_m, n_u, order='F')
R = Y > 0.5
pval = np.concatenate([abs(Y.ravel('F')), [0.001], [1]])
Y = Y * R
yval = np.concatenate([R.ravel('F'), [1], [0]])
params = np.concatenate([X.ravel(), Theta.ravel()])
if partId == '1':
mu, sigma2 = estimateGaussian(X)
out = formatter('%0.5f ', mu.ravel())
out += formatter('%0.5f ', sigma2.ravel())
elif partId == '2':
bestEpsilon, bestF1 = selectThreshold(yval, pval)
out = formatter('%0.5f ', bestEpsilon.ravel())
out += formatter('%0.5f ', bestF1.ravel())
elif partId == '3':
J, _ = cofiCostFunc(params, Y, R, n_u, n_m, n, 0)
out = formatter('%0.5f ', J.ravel())
elif partId == '4':
J, grad = cofiCostFunc(params, Y, R, n_u, n_m, n, 0)
X_grad = grad[:n_m * n].reshape(n_m, n)
Theta_grad = grad[n_m * n:].reshape(n_u, n)
out = formatter(
'%0.5f ',
np.concatenate([X_grad.ravel('F'),
Theta_grad.ravel('F')]))
elif partId == '5':
J, _ = cofiCostFunc(params, Y, R, n_u, n_m, n, 1.5)
out = formatter('%0.5f ', J.ravel())
elif partId == '6':
J, grad = cofiCostFunc(params, Y, R, n_u, n_m, n, 1.5)
X_grad = grad[:n_m * n].reshape(n_m, n)
Theta_grad = grad[n_m * n:].reshape(n_u, n)
out = formatter(
'%0.5f ',
np.concatenate([X_grad.ravel('F'),
Theta_grad.ravel('F')]))
return out
def ex7():
## Machine Learning Online Class
# Exercise 7 | Principle Component Analysis and K-Means Clustering
#
# Instructions
# ------------
#
# This file contains code that helps you get started on the
# exercise. You will need to complete the following functions:
#
# pca.m
# projectData.m
# recoverData.m
# computeCentroids.m
# findClosestCentroids.m
# kMeansInitCentroids.m
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
## Initialization
#clear ; close all; clc
## ================= Part 1: Find Closest Centroids ====================
# To help you implement K-Means, we have divided the learning algorithm
# into two functions -- findClosestCentroids and computeCentroids. In this
# part, you shoudl complete the code in the findClosestCentroids function.
#
print('Finding closest centroids.\n')
# Load an example dataset that we will be using
mat = scipy.io.loadmat('ex7data2.mat')
X = mat['X']
# Select an initial set of centroids
K = 3 # 3 Centroids
initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])
# Find the closest centroids for the examples using the
# initial_centroids
idx = findClosestCentroids(X, initial_centroids)
print('Closest centroids for the first 3 examples: ')
print(formatter(' %d', idx[:3] + 1))
print('\n(the closest centroids should be 1, 3, 2 respectively)')
print('Program paused. Press enter to continue.')
#pause
## ===================== Part 2: Compute Means =========================
# After implementing the closest centroids function, you should now
# complete the computeCentroids function.
#
print('\nComputing centroids means.\n')
# Compute means based on the closest centroids found in the previous part.
centroids = computeCentroids(X, idx, K)
print('Centroids computed after initial finding of closest centroids: ')
print(centroids)
print('\n(the centroids should be')
print(' [ 2.428301 3.157924 ]')
print(' [ 5.813503 2.633656 ]')
print(' [ 7.119387 3.616684 ]\n')
print('Program paused. Press enter to continue.')
#pause
## =================== Part 3: K-Means Clustering ======================
# After you have completed the two functions computeCentroids and
# findClosestCentroids, you have all the necessary pieces to run the
# kMeans algorithm. In this part, you will run the K-Means algorithm on
# the example dataset we have provided.
#
print('\nRunning K-Means clustering on example dataset.\n')
# Load an example dataset
mat = scipy.io.loadmat('ex7data2.mat')
X = mat['X']
# Settings for running K-Means
K = 3
max_iters = 10
# For consistency, here we set centroids to specific values
# but in practice you want to generate them automatically, such as by
# settings them to be random examples (as can be seen in
# kMeansInitCentroids).
initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])
# Run K-Means algorithm. The 'true' at the end tells our function to plot
# the progress of K-Means
centroids, idx = runkMeans('1', X, initial_centroids, max_iters, True)
print('\nK-Means Done.\n')
print('Program paused. Press enter to continue.')
#pause
## ============= Part 4: K-Means Clustering on Pixels ===============
# In this exercise, you will use K-Means to compress an image. To do this,
# you will first run K-Means on the colors of the pixels in the image and
# then you will map each pixel on to it's closest centroid.
#
# You should now complete the code in kMeansInitCentroids.m
#
print('\nRunning K-Means clustering on pixels from an image.\n')
# Load an image of a bird
A = matplotlib.image.imread('bird_small.png')
# If imread does not work for you, you can try instead
# load ('bird_small.mat')
A = A / 255 # Divide by 255 so that all values are in the range 0 - 1
# Size of the image
#img_size = size(A)
# Reshape the image into an Nx3 matrix where N = number of pixels.
# Each row will contain the Red, Green and Blue pixel values
# This gives us our dataset matrix X that we will use K-Means on.
X = A.reshape(-1, 3)
# Run your K-Means algorithm on this data
# You should try different values of K and max_iters here
K = 16
max_iters = 10
# When using K-Means, it is important the initialize the centroids
# randomly.
# You should complete the code in kMeansInitCentroids.m before proceeding
initial_centroids = kMeansInitCentroids(X, K)
# Run K-Means
centroids, idx = runkMeans('2', X, initial_centroids, max_iters)
print('Program paused. Press enter to continue.')
#pause
## ================= Part 5: Image Compression ======================
# In this part of the exercise, you will use the clusters of K-Means to
# compress an image. To do this, we first find the closest clusters for
# each example. After that, we
print('\nApplying K-Means to compress an image.\n')
# Find closest cluster members
idx = findClosestCentroids(X, centroids)
# Essentially, now we have represented the image X as in terms of the
# indices in idx.
# We can now recover the image from the indices (idx) by mapping each pixel
# (specified by it's index in idx) to the centroid value
X_recovered = centroids[idx,:].reshape(A.shape)
# Reshape the recovered image into proper dimensions
X_recovered = X_recovered.reshape(A.shape)
fig, ax = plt.subplots(1, 2, figsize=(8, 4))
# Display the original image
ax[0].imshow(A * 255)
ax[0].grid(False)
ax[0].set_title('Original')
# Display compressed image side by side
ax[1].imshow(X_recovered * 255)
ax[1].grid(False)
ax[1].set_title('Compressed, with %d colors' % K)
plt.savefig('figure3.png')
print('Program paused. Press enter to continue.\n')
def ex7_pca():
## Machine Learning Online Class
# Exercise 7 | Principle Component Analysis and K-Means Clustering
#
# Instructions
# ------------
#
# This file contains code that helps you get started on the
# exercise. You will need to complete the following functions:
#
# pca.m
# projectData.m
# recoverData.m
# computeCentroids.m
# findClosestCentroids.m
# kMeansInitCentroids.m
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
## Initialization
#clear ; close all; clc
## ================== Part 1: Load Example Dataset ===================
# We start this exercise by using a small dataset that is easily to
# visualize
#
print('Visualizing example dataset for PCA.\n')
# The following command loads the dataset. You should now have the
# variable X in your environment
mat = scipy.io.loadmat('ex7data1.mat')
X = mat['X']
# Visualize the example dataset
plt.plot(X[:, 0], X[:, 1], 'wo', ms=10, mec='b', mew=1)
plt.axis([0.5, 6.5, 2, 8])
plt.savefig('figure1.png')
print('Program paused. Press enter to continue.')
#pause
## =============== Part 2: Principal Component Analysis ===============
# You should now implement PCA, a dimension reduction technique. You
# should complete the code in pca.m
#
print('\nRunning PCA on example dataset.\n')
# Before running PCA, it is important to first normalize X
X_norm, mu, sigma = featureNormalize(X)
# Run PCA
U, S = pca(X_norm)
# Compute mu, the mean of the each feature
# Draw the eigenvectors centered at mean of data. These lines show the
# directions of maximum variations in the dataset.
#hold on
print(S)
print(U)
drawLine(mu, mu + 1.5 * np.dot(S[0], U[:,0].T))
drawLine(mu, mu + 1.5 * np.dot(S[1], U[:,1].T))
#hold off
plt.savefig('figure2.png')
print('Top eigenvector: ')
print(' U(:,1) = %f %f ' % (U[0,0], U[1,0]))
print('\n(you should expect to see -0.707107 -0.707107)')
print('Program paused. Press enter to continue.')
#pause
## =================== Part 3: Dimension Reduction ===================
# You should now implement the projection step to map the data onto the
# first k eigenvectors. The code will then plot the data in this reduced
# dimensional space. This will show you what the data looks like when
# using only the corresponding eigenvectors to reconstruct it.
#
# You should complete the code in projectData.m
#
print('\nDimension reduction on example dataset.\n\n')
# Plot the normalized dataset (returned from pca)
fig = plt.figure()
plt.plot(X_norm[:, 0], X_norm[:, 1], 'bo')
# Project the data onto K = 1 dimension
K = 1
Z = projectData(X_norm, U, K)
print('Projection of the first example: %f' % Z[0])
print('\n(this value should be about 1.481274)\n')
X_rec = recoverData(Z, U, K)
print('Approximation of the first example: %f %f' % (X_rec[0, 0], X_rec[0, 1]))
print('\n(this value should be about -1.047419 -1.047419)\n')
# Draw lines connecting the projected points to the original points
plt.plot(X_rec[:, 0], X_rec[:, 1], 'ro')
for i in range(X_norm.shape[0]):
drawLine(X_norm[i,:], X_rec[i,:])
#end
plt.savefig('figure3.png')
print('Program paused. Press enter to continue.\n')
#pause
## =============== Part 4: Loading and Visualizing Face Data =============
# We start the exercise by first loading and visualizing the dataset.
# The following code will load the dataset into your environment
#
print('\nLoading face dataset.\n\n')
# Load Face dataset
mat = scipy.io.loadmat('ex7faces.mat')
X = mat['X']
# Display the first 100 faces in the dataset
displayData(X[:100, :])
plt.savefig('figure4.png')
print('Program paused. Press enter to continue.\n')
#pause
## =========== Part 5: PCA on Face Data: Eigenfaces ===================
# Run PCA and visualize the eigenvectors which are in this case eigenfaces
# We display the first 36 eigenfaces.
#
print('\nRunning PCA on face dataset.\n(this mght take a minute or two ...)\n')
# Before running PCA, it is important to first normalize X by subtracting
# the mean value from each feature
X_norm, mu, sigma = featureNormalize(X)
# Run PCA
U, S = pca(X_norm)
# Visualize the top 36 eigenvectors found
displayData(U[:, :36].T)
plt.savefig('figure5.png')
print('Program paused. Press enter to continue.')
#pause
## ============= Part 6: Dimension Reduction for Faces =================
# Project images to the eigen space using the top k eigenvectors
# If you are applying a machine learning algorithm
print('\nDimension reduction for face dataset.\n')
K = 100
Z = projectData(X_norm, U, K)
print('The projected data Z has a size of: ')
print(formatter('%d ', Z.shape))
print('\n\nProgram paused. Press enter to continue.')
#pause
## ==== Part 7: Visualization of Faces after PCA Dimension Reduction ====
# Project images to the eigen space using the top K eigen vectors and
# visualize only using those K dimensions
# Compare to the original input, which is also displayed
print('\nVisualizing the projected (reduced dimension) faces.\n')
K = 100
X_rec = recoverData(Z, U, K)
# Display normalized data
#subplot(1, 2, 1)
displayData(X_norm[:100,:])
plt.gcf().suptitle('Original faces')
#axis square
plt.savefig('figure6.a.png')
# Display reconstructed data from only k eigenfaces
#subplot(1, 2, 2)
displayData(X_rec[:100,:])
plt.gcf().suptitle('Recovered faces')
#axis square
plt.savefig('figure6.b.png')
print('Program paused. Press enter to continue.')
#pause
## === Part 8(a): Optional (ungraded) Exercise: PCA for Visualization ===
# One useful application of PCA is to use it to visualize high-dimensional
# data. In the last K-Means exercise you ran K-Means on 3-dimensional
# pixel colors of an image. We first visualize this output in 3D, and then
# apply PCA to obtain a visualization in 2D.
#close all; close all; clc
# Re-load the image from the previous exercise and run K-Means on it
# For this to work, you need to complete the K-Means assignment first
A = matplotlib.image.imread('bird_small.png')
# If imread does not work for you, you can try instead
# load ('bird_small.mat')
A = A / 255
X = A.reshape(-1, 3)
K = 16
max_iters = 10
initial_centroids = kMeansInitCentroids(X, K)
centroids, idx = runkMeans('7', X, initial_centroids, max_iters)
# Sample 1000 random indexes (since working with all the data is
# too expensive. If you have a fast computer, you may increase this.
sel = np.random.choice(X.shape[0], size=1000)
# Setup Color Palette
#palette = hsv(K)
#colors = palette(idx(sel), :)
# Visualize the data and centroid memberships in 3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X[sel, 0], X[sel, 1], X[sel, 2], cmap='rainbow', c=idx[sel], s=8**2)
ax.set_title('Pixel dataset plotted in 3D. Color shows centroid memberships')
plt.savefig('figure8.png')
print('Program paused. Press enter to continue.')
#pause
## === Part 8(b): Optional (ungraded) Exercise: PCA for Visualization ===
# Use PCA to project this cloud to 2D for visualization
# Subtract the mean to use PCA
X_norm, mu, sigma = featureNormalize(X)
# PCA and project the data to 2D
U, S = pca(X_norm)
Z = projectData(X_norm, U, 2)
# Plot in 2D
fig = plt.figure()
plotDataPoints(Z[sel, :], [idx[sel]], K, 0)
plt.title('Pixel dataset plotted in 2D, using PCA for dimensionality reduction')
plt.savefig('figure9.png')
print('Program paused. Press enter to continue.\n')
def ex5():
## Machine Learning Online Class
# Exercise 5 | Regularized Linear Regression and Bias-Variance
#
# Instructions
# ------------
#
# This file contains code that helps you get started on the
# exercise. You will need to complete the following functions:
#
# linearRegCostFunction.m
# learningCurve.m
# validationCurve.m
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
## Initialization
#clear ; close all; clc
## =========== Part 1: Loading and Visualizing Data =============
# We start the exercise by first loading and visualizing the dataset.
# The following code will load the dataset into your environment and plot
# the data.
#
# Load Training Data
print('Loading and Visualizing Data ...')
# Load from ex5data1:
# You will have X, y, Xval, yval, Xtest, ytest in your environment
mat = scipy.io.loadmat('ex5data1.mat')
X = mat['X']
y = mat['y'].ravel()
Xval = mat['Xval']
yval = mat['yval'].ravel()
Xtest = mat['Xtest']
ytest = mat['ytest'].ravel()
# m = Number of examples
m = X.shape[0]
# Plot training data
plt.plot(X, y, marker='x', linestyle='None', ms=10, lw=1.5)
plt.xlabel('Change in water level (x)')
plt.ylabel('Water flowing out of the dam (y)')
plt.savefig('figure1.png')
print('Program paused. Press enter to continue.')
#pause;
## =========== Part 2: Regularized Linear Regression Cost =============
# You should now implement the cost function for regularized linear
# regression.
#
theta = np.array([1, 1])
J, _ = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1),
y, theta, 1)
print(
'Cost at theta = [1 ; 1]: %f \n(this value should be about 303.993192)'
% J)
print('Program paused. Press enter to continue.')
#pause;
## =========== Part 3: Regularized Linear Regression Gradient =============
# You should now implement the gradient for regularized linear
# regression.
#
theta = np.array([1, 1])
J, grad = linearRegCostFunction(
np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)
print(
'Gradient at theta = [1 ; 1]: [%f; %f] \n(this value should be about [-15.303016; 598.250744])'
% (grad[0], grad[1]))
print('Program paused. Press enter to continue.')
#pause;
## =========== Part 4: Train Linear Regression =============
# Once you have implemented the cost and gradient correctly, the
# trainLinearReg function will use your cost function to train
# regularized linear regression.
#
# Write Up Note: The data is non-linear, so this will not give a great
# fit.
#
fig = plt.figure()
# Train linear regression with lambda = 0
lambda_value = 0
theta = trainLinearReg(np.concatenate([np.ones((m, 1)), X], axis=1), y,
lambda_value)
# Plot fit over the data
plt.plot(X, y, marker='x', linestyle='None', ms=10, lw=1.5)
plt.xlabel('Change in water level (x)')
plt.ylabel('Water flowing out of the dam (y)')
plt.plot(X,
np.dot(np.concatenate([np.ones((m, 1)), X], axis=1), theta),
'--',
lw=2)
plt.savefig('figure2.png')
print('Program paused. Press enter to continue.')
#pause;
## =========== Part 5: Learning Curve for Linear Regression =============
# Next, you should implement the learningCurve function.
#
# Write Up Note: Since the model is underfitting the data, we expect to
# see a graph with "high bias" -- slide 8 in ML-advice.pdf
#
fig = plt.figure()
lambda_value = 0
error_train, error_val = learningCurve(
np.concatenate([np.ones((m, 1)), X], axis=1), y,
np.concatenate([np.ones((yval.size, 1)), Xval], axis=1), yval,
lambda_value)
plt.plot(np.arange(1, m + 1), error_train, np.arange(1, m + 1), error_val)
plt.title('Learning curve for linear regression')
plt.legend(['Train', 'Cross Validation'])
plt.xlabel('Number of training examples')
plt.ylabel('Error')
plt.axis([0, 13, 0, 150])
print('# Training Examples\tTrain Error\tCross Validation Error')
for i in range(m):
print(' \t%d\t\t%f\t%f' % (i, error_train[i], error_val[i]))
plt.savefig('figure3.png')
print('Program paused. Press enter to continue.')
#pause;
## =========== Part 6: Feature Mapping for Polynomial Regression =============
# One solution to this is to use polynomial regression. You should now
# complete polyFeatures to map each example into its powers
#
p = 8
# Map X onto Polynomial Features and Normalize
X_poly = polyFeatures(X, p)
X_poly, mu, sigma = featureNormalize(X_poly) # Normalize
X_poly = np.concatenate([np.ones((m, 1)), X_poly], axis=1) # Add Ones
# Map X_poly_test and normalize (using mu and sigma)
X_poly_test = polyFeatures(Xtest, p)
X_poly_test -= mu
X_poly_test /= sigma
X_poly_test = np.concatenate(
[np.ones((X_poly_test.shape[0], 1)), X_poly_test], axis=1) # Add Ones
# Map X_poly_val and normalize (using mu and sigma)
X_poly_val = polyFeatures(Xval, p)
X_poly_val -= mu
X_poly_val /= sigma
X_poly_val = np.concatenate(
[np.ones((X_poly_val.shape[0], 1)), X_poly_val], axis=1) # Add Ones
print('Normalized Training Example 1:')
print(formatter(' %f \n', X_poly[0, :]))
print('\nProgram paused. Press enter to continue.')
#pause;
## =========== Part 7: Learning Curve for Polynomial Regression =============
# Now, you will get to experiment with polynomial regression with multiple
# values of lambda. The code below runs polynomial regression with
# lambda = 0. You should try running the code with different values of
# lambda to see how the fit and learning curve change.
#
fig = plt.figure()
lambda_value = 0
theta = trainLinearReg(X_poly, y, lambda_value)
# Plot training data and fit
plt.plot(X, y, marker='x', ms=10, lw=1.5)
plotFit(np.min(X), np.max(X), mu, sigma, theta, p)
plt.xlabel('Change in water level (x)')
plt.ylabel('Water flowing out of the dam (y)')
plt.title('Polynomial Regression Fit (lambda = %f)' % lambda_value)
plt.figure()
error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval,
lambda_value)
plt.plot(np.arange(1, 1 + m), error_train, np.arange(1, 1 + m), error_val)
plt.title('Polynomial Regression Learning Curve (lambda = %f)' %
lambda_value)
plt.xlabel('Number of training examples')
plt.ylabel('Error')
plt.axis([0, 13, 0, 100])
plt.legend(['Train', 'Cross Validation'])
print('Polynomial Regression (lambda = %f)\n' % lambda_value)
print('# Training Examples\tTrain Error\tCross Validation Error')
for i in range(m):
print(' \t%d\t\t%f\t%f' % (i, error_train[i], error_val[i]))
plt.savefig('figure4.png')
print('Program paused. Press enter to continue.')
#pause;
## =========== Part 8: Validation for Selecting Lambda =============
# You will now implement validationCurve to test various values of
# lambda on a validation set. You will then use this to select the
# "best" lambda value.
#
fig = plt.figure()
lambda_vec, error_train, error_val = validationCurve(
X_poly, y, X_poly_val, yval)
plt.plot(lambda_vec, error_train, lambda_vec, error_val)
plt.legend(['Train', 'Cross Validation'])
plt.xlabel('lambda')
plt.ylabel('Error')
print('lambda\t\tTrain Error\tValidation Error')
for i in range(lambda_vec.size):
print(' %f\t%f\t%f' % (lambda_vec[i], error_train[i], error_val[i]))
plt.savefig('figure5.png')
print('Program paused. Press enter to continue.')
def ex6_spam():
## Machine Learning Online Class
# Exercise 6 | Spam Classification with SVMs
#
# Instructions
# ------------
#
# This file contains code that helps you get started on the
# exercise. You will need to complete the following functions:
#
# gaussianKernel.m
# dataset3Params.m
# processEmail.m
# emailFeatures.m
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
## Initialization
#clear ; close all; clc
## ==================== Part 1: Email Preprocessing ====================
# To use an SVM to classify emails into Spam v.s. Non-Spam, you first need
# to convert each email into a vector of features. In this part, you will
# implement the preprocessing steps for each email. You should
# complete the code in processEmail.m to produce a word indices vector
# for a given email.
print('\nPreprocessing sample email (emailSample1.txt)')
# Extract Features
file_contents = readFile('emailSample1.txt')
word_indices = processEmail(file_contents)
# Print Stats
print('Word Indices: ')
print(formatter(' %d', np.array(word_indices) + 1))
print('\n')
print('Program paused. Press enter to continue.')
#pause;
## ==================== Part 2: Feature Extraction ====================
# Now, you will convert each email into a vector of features in R^n.
# You should complete the code in emailFeatures.m to produce a feature
# vector for a given email.
print('\nExtracting features from sample email (emailSample1.txt)')
# Extract Features
file_contents = readFile('emailSample1.txt')
word_indices = processEmail(file_contents)
features = emailFeatures(word_indices)
# Print Stats
print('Length of feature vector: %d' % features.size)
print('Number of non-zero entries: %d' % np.sum(features > 0))
print('Program paused. Press enter to continue.')
#pause;
## =========== Part 3: Train Linear SVM for Spam Classification ========
# In this section, you will train a linear classifier to determine if an
# email is Spam or Not-Spam.
# Load the Spam Email dataset
# You will have X, y in your environment
mat = scipy.io.loadmat('spamTrain.mat')
X = mat['X'].astype(float)
y = mat['y'][:, 0]
print('\nTraining Linear SVM (Spam Classification)\n')
print('(this may take 1 to 2 minutes) ...\n')
C = 0.1
model = svmTrain(X, y, C, linearKernel)
p = svmPredict(model, X)
print('Training Accuracy: %f' % (np.mean(p == y) * 100))
## =================== Part 4: Test Spam Classification ================
# After training the classifier, we can evaluate it on a test set. We have
# included a test set in spamTest.mat
# Load the test dataset
# You will have Xtest, ytest in your environment
mat = scipy.io.loadmat('spamTest.mat')
Xtest = mat['Xtest'].astype(float)
ytest = mat['ytest'][:, 0]
print('\nEvaluating the trained Linear SVM on a test set ...\n')
p = svmPredict(model, Xtest)
print('Test Accuracy: %f\n' % (np.mean(p == ytest) * 100))
#pause;
## ================= Part 5: Top Predictors of Spam ====================
# Since the model we are training is a linear SVM, we can inspect the
# weights learned by the model to understand better how it is determining
# whether an email is spam or not. The following code finds the words with
# the highest weights in the classifier. Informally, the classifier
# 'thinks' that these words are the most likely indicators of spam.
#
# Sort the weights and obtin the vocabulary list
idx = np.argsort(model['w'])
top_idx = idx[-15:][::-1]
vocabList = getVocabList()
print('\nTop predictors of spam: ')
for word, w in zip(np.array(vocabList)[top_idx], model['w'][top_idx]):
print(' %-15s (%f)' % (word, w))
#end
print('\n')
print('\nProgram paused. Press enter to continue.')
#pause;
## =================== Part 6: Try Your Own Emails =====================
# Now that you've trained the spam classifier, you can use it on your own
# emails! In the starter code, we have included spamSample1.txt,
# spamSample2.txt, emailSample1.txt and emailSample2.txt as examples.
# The following code reads in one of these emails and then uses your
# learned SVM classifier to determine whether the email is Spam or
# Not Spam
# Set the file to be read in (change this to spamSample2.txt,
# emailSample1.txt or emailSample2.txt to see different predictions on
# different emails types). Try your own emails as well!
filename = 'spamSample1.txt'
# Read and predict
file_contents = readFile(filename)
word_indices = processEmail(file_contents)
x = emailFeatures(word_indices)
p = svmPredict(model, x.ravel())
print('\nProcessed %s\n\nSpam Classification: %d' % (filename, p))
print('(1 indicates spam, 0 indicates not spam)\n')
def ex4():
## Machine Learning Online Class - Exercise 4 Neural Network Learning
# Instructions
# ------------
#
# This file contains code that helps you get started on the
# linear exercise. You will need to complete the following functions
# in this exericse:
#
# sigmoidGradient.m
# randInitializeWeights.m
# nnCostFunction.m
#
# For this exercise, you will not need to change any code in this file,
# or any other files other than those mentioned above.
#
## Initialization
#clear ; close all; clc
## Setup the parameters you will use for this exercise
input_layer_size = 400 # 20x20 Input Images of Digits
hidden_layer_size = 25 # 25 hidden units
num_labels = 10 # 10 labels, from 1 to 10
# (note that we have mapped "0" to label 10)
## =========== Part 1: Loading and Visualizing Data =============
# We start the exercise by first loading and visualizing the dataset.
# You will be working with a dataset that contains handwritten digits.
#
# Load Training Data
print('Loading and Visualizing Data ...')
mat = scipy.io.loadmat('ex4data1.mat')
X = mat['X']
y = mat['y'].ravel()
m = X.shape[0]
# Randomly select 100 data points to display
sel = np.random.choice(m, 100, replace=False)
displayData(X[sel, :])
plt.savefig('figure1.png')
print('Program paused. Press enter to continue.')
#pause;
## ================ Part 2: Loading Parameters ================
# In this part of the exercise, we load some pre-initialized
# neural network parameters.
print('\nLoading Saved Neural Network Parameters ...')
# Load the weights into variables Theta1 and Theta2
mat = scipy.io.loadmat('ex4weights.mat')
Theta1 = mat['Theta1']
Theta2 = mat['Theta2']
# Unroll parameters
nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()])
## ================ Part 3: Compute Cost (Feedforward) ================
# To the neural network, you should first start by implementing the
# feedforward part of the neural network that returns the cost only. You
# should complete the code in nnCostFunction.m to return cost. After
# implementing the feedforward to compute the cost, you can verify that
# your implementation is correct by verifying that you get the same cost
# as us for the fixed debugging parameters.
#
# We suggest implementing the feedforward cost *without* regularization
# first so that it will be easier for you to debug. Later, in part 4, you
# will get to implement the regularized cost.
#
print('\nFeedforward Using Neural Network ...')
# Weight regularization parameter (we set this to 0 here).
lambda_value = 0
J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,
num_labels, X, y, lambda_value)[0]
print(
'Cost at parameters (loaded from ex4weights): %f \n(this value should be about 0.287629)'
% J)
print('\nProgram paused. Press enter to continue.')
#pause;
## =============== Part 4: Implement Regularization ===============
# Once your cost function implementation is correct, you should now
# continue to implement the regularization with the cost.
#
print('\nChecking Cost Function (w/ Regularization) ... ')
# Weight regularization parameter (we set this to 1 here).
lambda_value = 1
J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,
num_labels, X, y, lambda_value)[0]
print(
'Cost at parameters (loaded from ex4weights): %f \n(this value should be about 0.383770)'
% J)
print('Program paused. Press enter to continue.')
#pause;
## ================ Part 5: Sigmoid Gradient ================
# Before you start implementing the neural network, you will first
# implement the gradient for the sigmoid function. You should complete the
# code in the sigmoidGradient.m file.
#
print('\nEvaluating sigmoid gradient...')
g = sigmoidGradient(np.array([1, -0.5, 0, 0.5, 1]))
print('Sigmoid gradient evaluated at [1 -0.5 0 0.5 1]:')
print(formatter('%f ', g))
print('\n')
print('Program paused. Press enter to continue.')
#pause;
## ================ Part 6: Initializing Pameters ================
# In this part of the exercise, you will be starting to implment a two
# layer neural network that classifies digits. You will start by
# implementing a function to initialize the weights of the neural network
# (randInitializeWeights.m)
print('\nInitializing Neural Network Parameters ...')
initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size)
initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels)
# Unroll parameters
initial_nn_params = np.concatenate(
[initial_Theta1.ravel(),
initial_Theta2.ravel()])
## =============== Part 7: Implement Backpropagation ===============
# Once your cost matches up with ours, you should proceed to implement the
# backpropagation algorithm for the neural network. You should add to the
# code you've written in nnCostFunction.m to return the partial
# derivatives of the parameters.
#
print('\nChecking Backpropagation... ')
# Check gradients by running checkNNGradients
checkNNGradients()
print('\nProgram paused. Press enter to continue.')
#pause;
## =============== Part 8: Implement Regularization ===============
# Once your backpropagation implementation is correct, you should now
# continue to implement the regularization with the cost and gradient.
#
print('\nChecking Backpropagation (w/ Regularization) ... ')
# Check gradients by running checkNNGradients
lambda_value = 3
checkNNGradients(lambda_value)
# Also output the costFunction debugging values
debug_J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,
num_labels, X, y, lambda_value)[0]
print(
'\n\nCost at (fixed) debugging parameters (w/ lambda = 10): %f \n(this value should be about 0.576051)\n\n'
% debug_J)
print('Program paused. Press enter to continue.')
#pause;
## =================== Part 8: Training NN ===================
# You have now implemented all the code necessary to train a neural
# network. To train your neural network, we will now use "fmincg", which
# is a function which works similarly to "fminunc". Recall that these
# advanced optimizers are able to train our cost functions efficiently as
# long as we provide them with the gradient computations.
#
print('\nTraining Neural Network... ')
# After you have completed the assignment, change the MaxIter to a larger
# value to see how more training helps.
options = {'maxiter': 50}
# You should also try different values of lambda
lambda_value = 1
# Create "short hand" for the cost function to be minimized
costFunction = lambda p: nnCostFunction(
p, input_layer_size, hidden_layer_size, num_labels, X, y, lambda_value)
# Now, costFunction is a function that takes in only one argument (the
# neural network parameters)
res = optimize.minimize(costFunction,
initial_nn_params,
jac=True,
method='TNC',
options=options)
nn_params = res.x
# Obtain Theta1 and Theta2 back from nn_params
Theta1 = nn_params[0:hidden_layer_size * (input_layer_size + 1)].reshape(
hidden_layer_size, input_layer_size + 1)
Theta2 = nn_params[hidden_layer_size * (input_layer_size + 1):].reshape(
num_labels, hidden_layer_size + 1)
print('Program paused. Press enter to continue.')
#pause;
## ================= Part 9: Visualize Weights =================
# You can now "visualize" what the neural network is learning by
# displaying the hidden units to see what features they are capturing in
# the data.
print('\nVisualizing Neural Network... ')
displayData(Theta1[:, 1:])
plt.savefig('figure2.png')
print('\nProgram paused. Press enter to continue.')
#pause;
## ================= Part 10: Implement Predict =================
# After training the neural network, we would like to use it to predict
# the labels. You will now implement the "predict" function to use the
# neural network to predict the labels of the training set. This lets
# you compute the training set accuracy.
pred = predict(Theta1, Theta2, X)
print('\nTraining Set Accuracy: %f' % (np.mean(
(pred == y).astype(int)) * 100))