Last active
August 13, 2020 00:30
-
-
Save alik604/76861089ea95048239aa7ab7fed136d9 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
!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)) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment