ml-finance-python

perceptron.py

(1253B)

 from __future__ import print_function
 from sklearn import datasets
 import numpy as np
 
 # Import helper functions
 from mlfromscratch.utils import train_test_split, normalize, to_categorical, accuracy_score
 from mlfromscratch.deep_learning.activation_functions import Sigmoid
 from mlfromscratch.deep_learning.loss_functions import CrossEntropy 
 from mlfromscratch.utils import Plot
 from mlfromscratch.supervised_learning import Perceptron
 
 
 def main():
     data = datasets.load_digits()
     X = normalize(data.data)
     y = data.target
 
     # One-hot encoding of nominal y-values
     y = to_categorical(y)
 
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1)
 
     # Perceptron
     clf = Perceptron(n_iterations=5000,
         learning_rate=0.001, 
         loss=CrossEntropy,
         activation_function=Sigmoid)
     clf.fit(X_train, y_train)
 
     y_pred = np.argmax(clf.predict(X_test), axis=1)
     y_test = np.argmax(y_test, axis=1)
 
     accuracy = accuracy_score(y_test, y_pred)
 
     print ("Accuracy:", accuracy)
 
     # Reduce dimension to two using PCA and plot the results
     Plot().plot_in_2d(X_test, y_pred, title="Perceptron", accuracy=accuracy, legend_labels=np.unique(y))
 
 
 if __name__ == "__main__":
     main()