def crossValidation(train=400, test=100, la=1e-2, run=5):
mse = np.zeros(run)
coe = gd.gen_coefficients()
data_x, data_y, _ = gd.data_generator2(train + test, coe)
seq = np.arange(train + test)
seq = np.random.permutation(seq)
seq = seq.reshape(int((train + test) / test), test)
for i in range(run):
test_x = [data_x[:, j] for j in seq[i]]
test_x = np.transpose(np.matrix(np.ravel(test_x).reshape(test, 48)))
test_y = np.transpose(np.matrix([data_y[j, 0] for j in seq[i]]))
# print("test_x:",test_x.shape,"type:",type(test_x))
# print("test_y:",test_y.shape,"type:",type(test_y))
train_x = [data_x[:, j] for j in np.ravel(seq) if j not in seq[i]]
train_x = np.transpose(np.matrix(np.ravel(train_x).reshape(train, 48)))
train_y = np.transpose(
np.matrix([data_y[j, 0] for j in np.ravel(seq)
if j not in seq[i]]))
# print('train_x:',train_x.shape,"type:",type(train_x))
# print('train_y:',train_y.shape,"type:",type(train_y))
w = train_ridgeregression(train_x, train_y, la)
mse[i] += cal_mse(test_x, test_y, w)
return np.average(mse)
def crossValidation(train=400,test=100,la=1e-2,run=5):
mse = np.zeros(run)
coe=gd.gen_coefficients()
data_x,data_y,_=gd.data_generator2(train+test,coe)
seq = np.arange(train+test)
seq = np.random.permutation(seq)
seq = seq.reshape(int((train+test)/test),test)
for i in range(run):
test_x=[data_x[:,j] for j in seq[i]]
test_x= np.transpose(np.matrix(np.ravel(test_x).reshape(test,48)))
test_y= np.transpose(np.matrix([data_y[j,0] for j in seq[i]]))
# print("test_x:",test_x.shape,"type:",type(test_x))
# print("test_y:",test_y.shape,"type:",type(test_y))
train_x = [data_x[:,j] for j in np.ravel(seq) if j not in seq[i]]
train_x = np.transpose(np.matrix(np.ravel(train_x).reshape(train,48)))
train_y = np.transpose(np.matrix([data_y[j,0] for j in np.ravel(seq) if j not in seq[i]]))
# print('train_x:',train_x.shape,"type:",type(train_x))
# print('train_y:',train_y.shape,"type:",type(train_y))
w=train_ridgeregression(train_x,train_y,la)
mse[i]+=cal_mse(test_x,test_y,w)
return np.average(mse)
def holdout(train=400, test=100, la=1e-2):
coe = gd.gen_coefficients()
data_x, data_y, _ = gd.data_generator2(train + test, coe)
train_seq = random.sample([x for x in range(train + test)], train)
test_seq = [x for x in range(train + test) if x not in train_seq]
#training data
train_x = [data_x[:, j] for j in train_seq]
train_x = np.transpose(np.matrix(np.ravel(train_x).reshape(train, 48)))
train_y = np.transpose(np.matrix([data_y[j, 0] for j in train_seq]))
#testing data
test_x = [data_x[:, j] for j in test_seq]
test_x = np.transpose(np.matrix(np.ravel(test_x).reshape(test, 48)))
test_y = np.transpose(np.matrix([data_y[j, 0] for j in test_seq]))
w = train_ridgeregression(train_x, train_y, la)
# print("w:",w.shape,"data:",w)
return cal_mse(test_x, test_y, w)
def holdout(train=400,test=100,la=1e-2):
coe=gd.gen_coefficients()
data_x,data_y,_=gd.data_generator2(train+test,coe)
train_seq = random.sample([x for x in range(train+test)],train)
test_seq = [x for x in range(train+test) if x not in train_seq]
#training data
train_x=[data_x[:,j] for j in train_seq]
train_x = np.transpose(np.matrix(np.ravel(train_x).reshape(train,48)))
train_y=np.transpose(np.matrix([data_y[j,0] for j in train_seq]))
#testing data
test_x=[data_x[:,j] for j in test_seq]
test_x= np.transpose(np.matrix(np.ravel(test_x).reshape(test,48)))
test_y=np.transpose(np.matrix([data_y[j,0] for j in test_seq]))
w = train_ridgeregression(train_x,train_y,la)
# print("w:",w.shape,"data:",w)
return cal_mse(test_x,test_y,w)
