from MLlib.models import PolynomialRegression
from MLlib.optimizers import Adam
from MLlib.loss_func import MeanSquaredError
from MLlib.utils.misc_utils import read_data, printmat
X, Y = read_data('datasets/Polynomial_reg.txt')
polynomial_model = PolynomialRegression(3) # degree as user's choice
optimizer = Adam(0.01, MeanSquaredError)
polynomial_model.fit(X, Y, optimizer=optimizer, epochs=200, zeros=True)
printmat('predictions', polynomial_model.predict(X))
Z = polynomial_model.predict(X)
polynomial_model.save('test')
polynomial_model.plot(X, Y, Z, optimizer=optimizer, epochs=200, zeros=True)
from MLlib.models import LinearRegression
from MLlib.optimizers import Adam
from MLlib.loss_func import MeanSquaredError
from MLlib.utils.misc_utils import read_data, printmat
X, Y = read_data('datasets/linear_reg_00.txt')
linear_model = LinearRegression()
optimizer = Adam(0.01, MeanSquaredError)
linear_model.fit(X, Y, optimizer=optimizer, epochs=200, zeros=False)
printmat('predictions', linear_model.predict(X))
linear_model.save('test')
from MLlib import Tensor
from MLlib.regularizer import LinearRegWith_Regularization
from MLlib.regularizer import L1_Regularizer
from MLlib.optim import SGDWithMomentum
from MLlib.utils.misc_utils import printmat
import numpy as np
np.random.seed(5322)
x = Tensor.randn(10, 8) # (batch_size, features)
y = Tensor.randn(10, 1)
reg = LinearRegWith_Regularization(8,
L1_Regularizer,
optimizer=SGDWithMomentum,
Lambda=7)
# Regularizer,optimizer and Lambda as per user's choice
printmat("Total Loss", reg.fit(x, y, 800))