from sklearn import svm
import numpy as np
import random

data_violent = range(1,130)
data_nonviolent = range(1,130)

def strong_shuffle(numbers):
	random.shuffle(numbers)
	random.shuffle(numbers)
	random.shuffle(numbers)

strong_shuffle(data_violent)
strong_shuffle(data_nonviolent)

X_train = []
Y_train = []

X_test = []
Y_test = []

for i in data_nonviolent[0:80]:
    try:
        file_name = 'violent_features_NON_VIOLENT/nonvio_'+str(i)+'.txt'
        file_obj = open(file_name,'r')
        vif = np.loadtxt(file_obj)
        if vif.shape[0] == 336:# avoiding hd videos
            continue
        X_train.append(vif)
        Y_train.append(0)
        file_obj.close()
    except:
        continue
for i in data_violent[0:80]:
    try:
        file_name = 'violent_features_VIOLENT/vio_'+str(i)+'.txt'
        file_obj = open(file_name,'r')
        vif = np.loadtxt(file_obj)
        if vif.shape[0] == 336:# avoiding hd videos
            continue
        X_train.append(vif)
        Y_train.append(1)
        file_obj.close()
    except:
        continue
for i in data_nonviolent[81:]:
    try:
        file_name = 'violent_features_NON_VIOLENT/nonvio_'+str(i)+'.txt'
        file_obj = open(file_name,'r')
        vif = np.loadtxt(file_obj)
        if vif.shape[0] == 336:# avoiding hd videos
            continue
        X_test.append(vif)
        Y_test.append(0)
        file_obj.close()
    except:
        continue
for i in data_violent[81:]:
    try:
        file_name = 'violent_features_VIOLENT/vio_'+str(i)+'.txt'
        file_obj = open(file_name,'r')
        vif = np.loadtxt(file_obj)
        if vif.shape[0] == 336:# avoiding hd videos
            continue
        X_test.append(vif)
        Y_test.append(1)
        file_obj.close()
    except:
        continue
		# train contains the training set and test contains the testing set
classifiers = {}
for each_feature in range(0,252):
    # generating classifier for each feature
    train_data_X = []
    train_data_Y = []
    i = 0
    for each_set in X_train:
        train_data_X.append(each_set[each_feature])
        train_data_Y.append(Y_train[i])
        i += 1
    clf = svm.SVC(kernel = 'linear')
    clf.fit(np.array(train_data_X).reshape(-1,1),np.array(train_data_Y).reshape(-1,1))
    classifiers[each_feature] = clf

#calculate error for each classifier

w = [float(1)/(2*80)]*252
for t in range(0,10):
	# normalize weights
    w = [x/sum(w) for x in w]
    errors = [0.0]*252
    for each_feature in range(0,252):
        clf = classifiers[each_feature]
        i = 0
        preds = []
        for each_set in X_test:
			test_data_X = each_set[each_feature]
			test_data_Y = Y_test[i]
			i += 1
			preds.append(clf.predict(test_data_X.reshape(1,-1))[0])
        i = 0
        for each_pred in preds:
            errors[each_feature] += w[each_feature] * abs(each_pred-Y_test[i])
            i += 1


    min_error_classifier_index = np.where(errors == np.amin(errors))


    min_error_classifier_index = min_error_classifier_index[0][0]
    beta_change = errors[min_error_classifier_index]/(1.0 - errors[min_error_classifier_index])
    # calculate predictions for further use
    for each_feature in range(0,252):
        clf = classifiers[each_feature]
        i = 0
        preds = []
        for each_set in X_test:
            test_data_X = each_set[each_feature]
            test_data_Y = Y_test[i]
            i += 1
            preds.append(clf.predict(test_data_X.reshape(1,-1))[0])
        i = 0
    # update weights
        for each_set in X_test:
			print w[each_feature]
			w[each_feature] = w[each_feature] * (beta_change ** (1 - (abs(preds[i]-Y_test[i]))))
			i += 1
			print w[each_feature]

20c83d1505a13bb9ea3bf7b2ae7f272fb564117e
from sklearn import svm
import numpy as np
import random

data_violent = range(1,130)
data_nonviolent = range(1,130)

def strong_shuffle(numbers):
	random.shuffle(numbers)
	random.shuffle(numbers)
	random.shuffle(numbers)

strong_shuffle(data_violent)
strong_shuffle(data_nonviolent)

X_train = []
Y_train = []

X_test = []
Y_test = []

for i in data_nonviolent[0:80]:
    try:
        file_name = 'violent_features_NON_VIOLENT/nonvio_'+str(i)+'.txt'
        file_obj = open(file_name,'r')
        vif = np.loadtxt(file_obj)
        if vif.shape[0] == 336:# avoiding hd videos
            continue
        X_train.append(vif)
        Y_train.append(0)
        file_obj.close()
    except:
        continue
for i in data_violent[0:80]:
    try:
        file_name = 'violent_features_VIOLENT/vio_'+str(i)+'.txt'
        file_obj = open(file_name,'r')
        vif = np.loadtxt(file_obj)
        if vif.shape[0] == 336:# avoiding hd videos
            continue
        X_train.append(vif)
        Y_train.append(1)
        file_obj.close()
    except:
        continue
for i in data_nonviolent[81:]:
    try:
        file_name = 'violent_features_NON_VIOLENT/nonvio_'+str(i)+'.txt'
        file_obj = open(file_name,'r')
        vif = np.loadtxt(file_obj)
        if vif.shape[0] == 336:# avoiding hd videos
            continue
        X_test.append(vif)
        Y_test.append(0)
        file_obj.close()
    except:
        continue
for i in data_violent[81:]:
    try:
        file_name = 'violent_features_VIOLENT/vio_'+str(i)+'.txt'
        file_obj = open(file_name,'r')
        vif = np.loadtxt(file_obj)
        if vif.shape[0] == 336:# avoiding hd videos
            continue
        X_test.append(vif)
        Y_test.append(1)
        file_obj.close()
    except:
        continue
		# train contains the training set and test contains the testing set
classifiers = {}
for each_feature in range(0,252):
    # generating classifier for each feature
    train_data_X = []
    train_data_Y = []
    i = 0
    for each_set in X_train:
        train_data_X.append(each_set[each_feature])
        train_data_Y.append(Y_train[i])
        i += 1
    clf = svm.SVC(kernel = 'linear')
    clf.fit(np.array(train_data_X).reshape(-1,1),np.array(train_data_Y).reshape(-1,1))
    classifiers[each_feature] = clf

#calculate error for each classifier

w = [float(1)/(2*80)]*252
for t in range(0,10):
	# normalize weights
    w = [x/sum(w) for x in w]
    errors = [0.0]*252
    for each_feature in range(0,252):
        clf = classifiers[each_feature]
        i = 0
        preds = []
        for each_set in X_test:
			test_data_X = each_set[each_feature]
			test_data_Y = Y_test[i]
			i += 1
			preds.append(clf.predict(test_data_X.reshape(1,-1))[0])
        i = 0
        for each_pred in preds:
            errors[each_feature] += w[each_feature] * abs(each_pred-Y_test[i])
            i += 1


    min_error_classifier_index = np.where(errors == np.amin(errors))


    min_error_classifier_index = min_error_classifier_index[0][0]
    beta_change = errors[min_error_classifier_index]/(1.0 - errors[min_error_classifier_index])
    # calculate predictions for further use
    for each_feature in range(0,252):
        clf = classifiers[each_feature]
        i = 0
        preds = []
        for each_set in X_test:
            test_data_X = each_set[each_feature]
            test_data_Y = Y_test[i]
            i += 1
            preds.append(clf.predict(test_data_X.reshape(1,-1))[0])
        i = 0
    # update weights
        for each_set in X_test:
			print w[each_feature]
			w[each_feature] = w[each_feature] * (beta_change ** (1 - (abs(preds[i]-Y_test[i]))))
			i += 1
			print w[each_feature]
