05-nmf-learns-the-parts-of-images.py

'''
NMF learns the parts of images

Now use what you've learned about NMF to decompose the digits dataset. You are again given the digit images as a 2D array samples. This time, you are also provided with a function show_as_image() that displays the image encoded by any 1D array:

def show_as_image(sample):
    bitmap = sample.reshape((13, 8))
    plt.figure()
    plt.imshow(bitmap, cmap='gray', interpolation='nearest')
    plt.colorbar()
    plt.show()
After you are done, take a moment to look through the plots and notice how NMF has expressed the digit as a sum of the components!

INSTRUCTIONS
100XP
Import NMF from sklearn.decomposition.
Create an NMF instance called model with 7 components. (7 is the number of cells in an LED display).
Apply the .fit_transform() method of model to samples. Assign the result to features.
To each component of the model (accessed via model.components_), apply the show_as_image() function to that component inside the loop.
Assign the row 0 of features to digit_features.
Print digit_features.
'''
# Import NMF
from sklearn.decomposition import NMF

# Create an NMF model: model
model = NMF(n_components=7)

# Apply fit_transform to samples: features
features = model.fit_transform(samples)

# Call show_as_image on each component
for component in model.components_:
    show_as_image(component)

# Assign the 0th row of features: digit_features
digit_features = features[0,:]

# Print digit_features
print(digit_features)