mnist - get data.py

!pip install mnist
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 
from sklearn import metrics
import mnist

# from hmmlearn.hmm import GaussianHMM, MultinomialHMM

X_train = mnist.train_images()
y_train = mnist.train_labels()
X_test = mnist.test_images()
y_test = mnist.test_labels()

# Normalize the images.
X_train = (X_train / 255) - 0.5
X_test = (X_test / 255) - 0.5

# Flatten the images.
X_train = X_train.reshape((-1, 784))
X_test = X_test.reshape((-1, 784))

print(X_train.shape) # (60000, 784)
print(X_test.shape)  # (10000, 784)

# Apply a learning algorithm
print("Applying a learning algorithm...")
clf = RandomForestClassifier(n_estimators=75,n_jobs=4)
clf.fit(X_train, y_train)

# Make a prediction
print("Making predictions...")
y_pred = clf.predict(X_test)

#print y_pred

# Evaluate the prediction
print("Evaluating results...")
print("Precision: \t", metrics.precision_score(y_test, y_pred, average='micro'))
print("Recall: \t", metrics.recall_score(y_test, y_pred, average='micro'))
print("F1 score: \t", metrics.f1_score(y_test, y_pred, average='micro'))
print("Mean accuracy: \t", clf.score(X_test, y_test))