Assignment for Machine Learning

Here is the example code for chapter 3 from Jupyter notebook.

*******code********

from sklearn.datasets import fetch_mldata

mnist = fetch_mldata('MNIST original')

mnist

X, y = mnist["data"], mnist["target"]

X.shape

y.shape

28*28

%matplotlib inline

import matplotlib

import matplotlib.pyplot as plt

some_digit = X[36000]

some_digit_image = some_digit.reshape(28, 28)

plt.imshow(some_digit_image, cmap = matplotlib.cm.binary,

interpolation="nearest")

plt.axis("off")

save_fig("some_digit_plot")

plt.show()

def plot_digit(data):

image = data.reshape(28, 28)

plt.imshow(image, cmap = matplotlib.cm.binary,

interpolation="nearest")

plt.axis("off")

# EXTRA

def plot_digits(instances, images_per_row=10, **options):

size = 28

images_per_row = min(len(instances), images_per_row)

images = [instance.reshape(size,size) for instance in instances]

n_rows = (len(instances) - 1) // images_per_row + 1

row_images = []

n_empty = n_rows * images_per_row - len(instances)

images.append(np.zeros((size, size * n_empty)))

for row in range(n_rows):

rimages = images[row * images_per_row : (row + 1) * images_per_row]

row_images.append(np.concatenate(rimages, axis=1))

image = np.concatenate(row_images, axis=0)

plt.imshow(image, cmap = matplotlib.cm.binary, **options)

plt.axis("off")

plt.figure(figsize=(9,9))

example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]

plot_digits(example_images, images_per_row=10)

save_fig("more_digits_plot")

plt.show()

y[36000]

X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]

import numpy as np

shuffle_index = np.random.permutation(60000)

X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]