Train an Estimator to learn how to encode any integer to binary

learn_binary.py

"""
Train an Estimator to learn how to encode integer to binary
"""

import numpy as np

X = np.array([
  [1, -1],  [1, 0],  [1, 1],
  [0, -1],  [0, 0],  [0, 1],
  [-1, -1], [-1, 0], [-1, 1],
])

#  Top
#  Middle  
#  Bottom  
#  Corner
y = np.array([
  (1, 0, 0, 1), 
  (1, 0, 0, 0), 
  (1, 0, 0, 1), 

  (0, 1, 0, 0), 
  (0, 1, 0, 0), 
  (0, 1, 0, 0), 

  (0, 0, 1, 1), 
  (0, 0, 1, 0), 
  (0, 0, 1, 1), 
])

from sklearn.svm import SVC
clf = SVC()

from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()

clf.fit(X, y) 
print(clf.predict([[-0.8, -1]]))

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

h = .02  # step size in the mesh

# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                     np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("3-Class classification (k = %i, weights = '%s')"
          % (n_neighbors, weights))

plt.show()