ml-finance-python

support_vector_machine.py

(969B)

 from __future__ import division, print_function
 import numpy as np
 from sklearn import datasets
 
 # Import helper functions
 from mlfromscratch.utils import train_test_split, normalize, accuracy_score, Plot
 from mlfromscratch.utils.kernels import *
 from mlfromscratch.supervised_learning import SupportVectorMachine
 
 def main():
     data = datasets.load_iris()
     X = normalize(data.data[data.target != 0])
     y = data.target[data.target != 0]
     y[y == 1] = -1
     y[y == 2] = 1
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
 
     clf = SupportVectorMachine(kernel=polynomial_kernel, power=4, coef=1)
     clf.fit(X_train, y_train)
     y_pred = clf.predict(X_test)
 
     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="Support Vector Machine", accuracy=accuracy)
 
 
 if __name__ == "__main__":
     main()