Training and Predicting using SVM.

import numpy as np
from sklearn.svm import SVC


DATASET = '../rsc/q2_dataset.txt'

# Read dataset and return 3 lists corresponding to classification, x and y


def train_predict(constant, dataset):

  fid = open(dataset, 'r')

  x = []
  y = []

  for line in fid:
    x.append([float(line.split()[1][2:]), float(line.split()[2][2:])])
    y.append(float(line.split()[0]))

  fid.close()

  clf = SVC(C=constant, kernel='linear')
  clf.fit(np.array(x), np.array(y))

  # Predicting Random values.
  val = clf.predict(np.array([[8, 8], [2, 8], [5, 6], [5, 3], [6, 8]]))

  alphas = np.abs(clf.dual_coef_)

  print(alphas)  # Alpha
  print(clf.coef_)  # Weights
  print(val)  # Predicted classifications.


def main():
  train_predict(1, DATASET)
  print('-----')
  train_predict(3, DATASET)


main()