from __future__ import print_function
import time
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.datasets import load_digits
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import LabelBinarizer
from ann import ANN

# import the simplified mnist dataset form scikit learn
digits = load_digits()

# get the input vectors (X is a vector of vectors of type int)
X = digits.data

# get the output vector ( y is a vector of type int)
y = digits.target

print("X.shape", X.shape)
print("y.shape", y.shape)

# normalize input into [0, 1]
X -= X.min()
X /= X.max()

# split data into training and testing 75% of examples are used for training and 25% are used for testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

# binarize the labels from a number into a vector with a 1 at that index
# ex: label 4 -> binarized [0 0 0 0 1 0 0 0 0 0]
# ex: label 7 -> binarized [0 0 0 0 0 0 0 1 0 0]
labels_train = LabelBinarizer().fit_transform(y_train)
labels_test = LabelBinarizer().fit_transform(y_test)

# convert from numpy to normal python list for our simple implementation
X_train_l = X_train.tolist()
labels_train_l = labels_train.tolist()

# create the artificial network with:
# 1 input layer of size 64 (the images are 8x8 gray pixels)
# 1 hidden layer of size 100
# 1 output layer of size 10 (the labels of digits are 0 to 9)
nn = ANN([64, 100, 10])

# see how long training takes
startTime = time.time()

# train it
nn.train(10, X_train_l, labels_train_l)

elapsedTime = time.time() - startTime
print("Training took {} seconds", int(elapsedTime))

# compute the predictions
predictions = []
for i in range(X_test.shape[0]):
	o = nn.predict(X_test[i])
	# the inverse of the binarization would be taking the maximum argument index
	# ex: [.1 .1 .1 .1 .9 .1 .1 .1 .1 .1] -> 4
	# ex: [.1 .1 .1 .1 .1 .1 .1 .9 .1 .1] -> 7
	predictions.append(np.argmax(o))

# compute a confusion matrix
print("confusion matrix")
print(confusion_matrix(y_test, predictions))

# show a classification report
print("classification report")
print(classification_report(y_test, predictions))

# 94%-97% precision 94-97% recall
Add mnist 8by8 classifier with scikit dataset
Add mnist 8by8 classifier with scikit dataset
