读取多张MNIST图片与利用BaseEstimator基类创建分类器

def plot_digit(data):
    img = data.reshape(28,28)
    plt.imshow(img,cmap=matplotlib.cm.binary,interpolation=‘nearest‘)
    plt.axis(‘off‘)

import matplotlib.pyplot as plt
import matplotlib
some_digit = X[36000]
plot_digit(some_digit)

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‘)

import numpy as np
import os

# to make this notebook‘s output stable across runs
np.random.seed(42)

# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc(‘axes‘, labelsize=14)
mpl.rc(‘xtick‘, labelsize=12)
mpl.rc(‘ytick‘, labelsize=12)

# Where to save the figures
PROJECT_ROOT_DIR = "."
#CHAPTER_ID = "classification"

def save_fig(fig_id, tight_layout=True):
    path = os.path.join(PROJECT_ROOT_DIR, "images", fig_id + ".png")
    print("Saving figure", fig_id)
    if tight_layout:
        plt.tight_layout()
    plt.savefig(path, format=‘png‘, dpi=300)

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()

from sklearn.base import BaseEstimator
class Never5Classifier(BaseEstimator):
    def fit(self,X,y = None):
        pass
    def predict(self,X):
        return np.zeros((len(X),1),dtype = bool)

never_5_clf = Never5Classifier()
cross_val_score(never_5_clf,X_train,y_train_5,cv = 3,scoring=‘accuracy‘)