def classify_binary(dataset_path):
X, Y = getBinaryData(dataset_path)
X0, X1 = X[Y == 0, :], X[Y == 1, :]
X1 = np.repeat(X1, 9, axis=0) #duplicate the data 9x
X, Y = np.vstack([X0, X1]), np.array([0] * len(X0) + [1] * len(X1))
#this is for classify a binary classification
model = ANN(100)
model.fit_2class(X, Y, show_fig=True)
model.score(X, Y)
def main():
X, Y = getBinaryData()
X0 = X[Y == 0, :]
X1 = X[Y == 1, :]
X1 = np.repeat(X1, 9, axis=0)
X = np.vstack([X0, X1])
Y = np.array([0] * len(X0) + [1] * len(X1))
model = ANN(100, activation_func='relu')
model.fit(X, Y, show_fig=True)
def main():
X, Y = getBinaryData()
X0, Y0 = X[Y != 1, :], Y[Y != 1]
X1 = X[Y == 1, :]
X1 = np.repeat(X1, 9, axis=0)
X = np.vstack([X0, X1])
Y = np.concatenate((Y0, [1] * len(X1)))
model = ANN(100)
model.fit(X, Y, show_fig=True)
def main():
X, Y = getBinaryData()
X0 = X[Y==0, :]
X1 = X[Y==1, :]
X1 = np.repeat(X1, 9, axis=0)
X = np.vstack([X0, X1])
Y = np.array([0]*len(X0) + [1]*len(X1))
model = ANN(100)
model.fit(X, Y, show_fig=True)
def main():
X, Y = getBinaryData()
# print("X.shape"+str(X.shape))
# print("Y.shape"+str(Y.shape))
# X0 = X[Y==0]
# X1 = X[Y==1]
# print("X0.shape"+str(X0.shape))
# print("X1.shape"+str(X1.shape))
model = ANN(100)
model.fit(X,Y,show_fig = True)
def main(): X, Y = getBinaryData() X0 = X[Y==0, :] X1 = X[Y==1, :] X1 = np.repeat(X1, 9, axis=0) X = np.vstack([X0, X1]) Y = np.array([0]*len(X0)+[1]*len(X1)) model = LogisticModel() model.fit(X, Y, show_fig=True) model.score()
def main():
X, Y = getBinaryData()
X0 = X[Y==0, :]
X1 = X[Y==1, :]
X1 = np.repeat(X1, 9, axis=0)
X = np.vstack([X0, X1])
Y = np.array([0]*len(X0) + [1]*len(X1))
# N, D = X.shape
# print "N:", N
# print "p(Y=0):", np.sum(Y == 0) / float(N), "p(Y=1):", np.sum(Y == 1) / float(N)
model = LogisticModel()
model.fit(X, Y, show_fig=True)
model.score(X, Y)
def main():
X, Y = getBinaryData()
X0 = X[Y == 0, :]
X1 = X[Y == 1, :]
X1 = np.repeat(X1, 9, axis=0)
X = np.vstack([X0, X1])
Y = np.array([0] * len(X0) + [1] * len(X1))
model = LogisticModel()
model.fit(X, Y, show_fig=True)
model.score(X, Y)
scores = cross_val_score(model, X, Y, cv=5)
print("score mean:", np.mean(scores), "stdev:", np.std(scores))
def main():
X, Y = getBinaryData()
X0 = X[Y == 0, :]
X1 = X[Y == 1, :]
X1 = np.repeat(X1, 9, axis=0)
X = np.vstack([X0, X1])
Y = np.array([0] * len(X0) + [1] * len(X1))
# N, D = X.shape
# print "N:", N
# print "p(Y=0):", np.sum(Y == 0) / float(N), "p(Y=1):", np.sum(Y == 1) / float(N)
model = LogisticModel()
model.fit(X, Y, show_fig=True)
model.score(X, Y)
def main():
X, Y = getBinaryData()
X0 = X[
Y ==
0, :] # This means everytime the value of Y == 0, then select the entire corresponding row in X. Done due to binary classification
X1 = X[
Y ==
1, :] # This means everytime the value of Y == 1, then select the entire corresponding row in X. Done due to binary classification
X1 = np.repeat(
X1, 9, axis=0
) # To address label imbalance, we lengthen label 1 by repeating it 9 times
X = np.vstack([X0, X1])
Y = np.array([0] * len(X0) + [1] * len(X1))
model = ANN(100)
model.fit(X, Y, show_fig=True)
model.score(X, Y)
def main():
X, Y = getBinaryData()
X0 = X[Y==0, :]
X1 = X[Y==1, :]
X1 = np.repeat(X1, 9, axis=0)
X = np.vstack([X0, X1])
Y = np.array([0]*len(X0) + [1]*len(X1))
X, Y = shuffle(X, Y)
K = len(np.unique(Y))
N = len(Y)
T = np.zeros((N, K))
for i in range(N):
T[i, Y[i]] = 1 # one hot encoding for targets
m = nn.NeuralNetwork(numHiddenLayer=1,numHiddenUnits=100,actFunc="Sigmoid")
trainCost, validCost, accTrain, accValid = m.train(X, T, epochs=10000, learning_rate=5*10e-7)
print("Final Train Accuracy {}".format(accTrain))
print("Final Valid Accuracy {}".format(accValid))
legend1, = plt.plot(trainCost, label='training error')
legend2, = plt.plot(validCost, label='validation error')
plt.legend([legend1, legend2])
plt.show()
print("best validation error:", best_validation_error)
if show_fig:
plt.plot(costs)
plt.show()
def forward(self, X):
return util.sigmoid(X.dot(self.w + self.b))
def predict(self, X, Y):
pY = self.forward(X)
return np.round(pY)
def score(self, X, Y):
predictions = self.predict(X)
return 1 - util.error_rate(Y, predictions)
if __name__ == '__main__':
X, Y = util.getBinaryData()
# balance classes (class 1 has 9x less occurrences than class 0)
X0 = X[Y == 0, :]
X1 = X[Y == 1, :]
X1 = np.repeat(X1, 9, axis=0)
X = np.vstack([X0, X1])
Y = np.array([0] * len(X0) + [1] * len(X1))
model = LogisticModel()
model.fit(X, Y, show_fig=True)
model.score(X, Y)
def predict(self, X):
pY = self.forward(X)
return np.round(pY)
def score(self, X, Y):
prediction = self.predict(X)
return 1 - error_rate(Y, prediction)
# In[3]:
import datetime
start = datetime.datetime.now()
X, Y = getBinaryData(
facial_data_csv_kaggle='c:/Users/Denis/Desktop/fer2013/fer2013')
X0 = X[Y == 0, :]
X1 = X[Y == 1, :]
X1 = np.repeat(X1, 9, axis=0)
X = np.vstack([X0, X1])
Y = np.array([0] * len(X0) + [1] * len(X1))
model = LogisticModel()
model.fit(X, Y, show_fig=True)
model.score(X, Y)
end = datetime.datetime.now()
took = end - start
print("This training took %.1f seconds" % (took.seconds))
