In [ ]:

    

from sklearn.datasetsimportload_digits
digits=load_digits()
fromsklearn.datasetsimportload_breast_cancer
breast_cancer=load_breast_cancer()

fromsklearn.cross_validationimportcross_val_score
fromsklearn.naive_bayesimportBernoulliNB,MultinomialNB,GaussianNB

b_NB=BernoulliNB()
m_NB=MultinomialNB()
g_NB=GaussianNB()

printcross_val_score(b_NB,breast_cancer.data,breast_cancer.target).mean()
printcross_val_score(m_NB,breast_cancer.data,breast_cancer.target).mean()
printcross_val_score(g_NB,breast_cancer.data,breast_cancer.target).mean()


    

In [ ]:

    

fromsklearn.datasetsimportload_breast_cancer
breast_cancer=load_breast_cancer()


    

In [ ]:

    

importsklearn
sklearn.__version__


    

In [3]:

    

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.utils import shuffle
import mahotas as mh

original_img=np.array(mh.imread('1.jpeg'),dtype=np.float64)/255

original_dimensions=tuple(original_img.shape)
width,height,depth=tuple(original_img.shape)
image_flattened=np.reshape(original_img,(width*height,depth))
print image_flattened.shape


    

    




(480000, 3)

In [4]:

    

image_array_sample=shuffle(image_flattened,random_state=0)[:5000]


    

In [5]:

    

print image_array_sample.shape


    

    




(50000, 3)

In [31]:

    

estimator=KMeans(n_clusters=6,random_state=0)
estimator.fit(image_array_sample)
cluster_assignments=estimator.predict(image_flattened)


    

In [32]:

    

cluster_assignments = estimator.predict(image_flattened)
compressed_palette = estimator.cluster_centers_
compressed_img = np.zeros((width, height, compressed_palette.shape[1]))
label_idx = 0
for i in range(width):
    for j in range(height):
        compressed_img[i][j] = compressed_palette[cluster_assignments[label_idx]]
        label_idx += 1
plt.subplot(122)
plt.title('Original Image')
plt.imshow(original_img)
plt.axis('off')
plt.subplot(121)
plt.title('Compressed Image')
plt.imshow(compressed_img)
plt.axis('off')
plt.show()


    

In [11]:

    

original_img.size


    

    
Out[11]:





1440000

In [10]:

    

compressed_img.size


    

    
Out[10]:





1440000