mypart1.py

import numpy as n
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as graph

# Function to read the features from file
def read_features(par_filename):
	vl = []
	with open(par_filename,"r") as file_lines:
		#features = [[float(i) for i in line.split()] for line in file_lines]
		for line in file_lines:
			vl.append(line.split())
	file_lines.close()
	for r in vl:
		del r[12]
	return vl;
	
# Function to read the lables from file
def read_labels(par_filename):
	vl = []
	with open(par_filename,"r") as file_lines:
		for line in file_lines:
			vl.append(line.split())
	file_lines.close()
	ll = []
	for r in vl:
		ll.append(r[12])
	return ll;
	
# Function to compute the classification using SVM
def compute_SVC(train_f,train_l):
	C=1.0
	cache_size=200
	class_weight=None
	coef0=0.0
	decision_function_shape=None
	degree=3
	gamma='auto'
	kernel='rbf'
	max_iter=-1
	probability=False
	random_state=None
	shrinking=True
	tol=0.001
	verbose=False
	c = svm.SVC(kernel='linear')
	c.fit(train_f,train_l)
	return c;
	
# Function to calculate the accuracy
def compute_accuracy(test_f,test_l,c):
	pred = c.predict(test_f)
	print pred
	pred_accu = accuracy_score(test_l,pred)
	return pred_accu;
	
# Function to compute the confusion matrix
def compute_confusion_matrix(test_f,test_l,c):
	pred = c.predict(test_f)
	x = confusion_matrix(test_l,pred)
	return x;

# Starting of the flow of program
read_data_features_train = read_features("plrx.txt");
read_data_labels_train = read_labels("plrx.txt");
model_svc = compute_SVC(read_data_features_train,read_data_labels_train);
accu_percent = compute_accuracy(read_data_features_train,read_data_labels_train,model_svc)*100;
print "Accuracy obtained over the whole training set is %0.6f %% ." % (accu_percent)
#conf_mat = compute_confusion_matrix(read_data_features_train,read_data_labels_train,model_svc);
#print conf_mat