def fit(self, x: np.ndarray, y: np.ndarray) -> None:
M, N = x.shape
self.v = np.random.normal(0, 0.1, [self.k, N])
self.w = np.random.normal(0, 0.1, N)
for _iter in range(self.max_iter):
dv = np.zeros([self.k, N])
dw = np.zeros(N)
db = 0
y_hat = self.predict(x)
if self.verbose:
print("################################")
print("iter{}, 训练误差:".format(_iter), compute_mse(y, y_hat))
for m in range(M):
diff = y[m] - y_hat[m]
dw += -0.5 * diff * x[m]
db += -0.5 * diff
dv += -0.5 * diff * (np.matmul(
np.matmul(self.v, x[m]).reshape(-1, 1), x[m].reshape(
1, -1)) - x[m]**2 * self.v)
self.w -= self.learning_rate * dw / m
self.b -= self.learning_rate * db / m
self.v -= self.learning_rate * dv / m
from algs.nn_algoritms.common.model import Model
from algs.nn_algoritms.common.layer import Dense
import numpy as np
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from algs.utils import compute_mse
x, y = load_boston(return_X_y=1)
x = (x - np.min(x, axis=0)) / (np.max(x, axis=0) - np.min(x, axis=0))
y = y.reshape(-1, 1)
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.3,
random_state=50)
model = Model(lr=0.01, epoch=1500, loss="Mse")
model.add(Dense(activation='leakyrelu', units=64))
model.add(Dense(activation='tanh', units=32))
model.add(Dense(activation='linear', units=1))
model.fit(x_train, y_train)
y_predict = model.predict(x_test).reshape(-1)
print(compute_mse(y_predict, y_test))
import matplotlib.pyplot as plt
plt.plot(model.history['loss'])
from sklearn.model_selection import train_test_split
from algs.ml_algorithms.fm import FM
from algs.utils import compute_mse
x, y = load_boston(return_X_y=1)
# 分桶为稀疏矩阵
######################################################################
x_new = []
for col in range(x.shape[1]):
tmp = np.zeros([len(x), 16])
_max = max(x[:, col])
_min = min(x[:, col])
for row in range(x.shape[0]):
_ = int(round((x[row][col] - _min) / ((_max - _min) / 15), 0))
tmp[row, _] = 1
x_new.append(tmp)
x = np.concatenate(x_new, axis=1)
######################################################################
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.3,
random_state=50)
fm = FM(k=15, learning_rate=0.03)
fm.fit(x_train, y_train)
y_predict = fm.predict(x_test.astype(np.float32))
print("验证集误差：", compute_mse(y_predict, y_test))