(506,)fromscipyimportstats slope, intercept, r_value, p_value, std_err = stats.linregress(lstat, y)print(slope, intercept, r_value, p_value, std_err) -0.950049353757990934.55384087938311-0.7376627261740155.081103394387796e-880.03873341621263942print("r-squared:", r_value**2) r-squared:0.54414629758...
print("Mean squared error: %.2f" % mean_squared_error(y_test, y_test_pred1)) # r2 score,0,1之间,越接近1说明模型越好,越接近0说明模型越差 print('Variance score: %.2f' % r2_score(y_test, y_test_pred1), '\n') # fit_intercept 为 False model2 = Pipeline([('poly', PolynomialF...
rm = x[0:,5] slope, intercept, r_value, p_value, std_err = stats.linregress(rm, y) print(slope, intercept, r_value, p_value, std_err) print("r-squared:", r_value**2) ## 9.102108981180308 -34.670620776438554 0.6953599470715394 2.48722887100781e-74 0.4190265601213402 ## r-squared: 0.483...
以下代码块打印出性能指标: rm = x[0:,5]slope, intercept, r_value, p_value, std_err = stats.linregress(rm, y)print(slope, intercept, r_value, p_value, std_err)print("r-squared:", r_value**2)## 9.102108981180308 -34.670620776438554 0.6953599470715394 2.48722887100781e-74 0.4190265601213402#...
确保正确导入Numpy库:在代码的开头添加import numpy as np,确保正确导入Numpy库。 检查数据类型:Numpy是一个强类型的库,它要求数组中的元素具有相同的数据类型。如果在进行数组操作时出现类型错误,可以通过dtype属性来检查数组的数据类型。例如,使用arr.dtype可以查看数组arr的数据类型。 转换数据类型:如果发现数据类型不...
并将其命名为 npimportnumpyasnp# 从 numpy.testing 模块中导入 assert_almost_equal 函数fromnumpy.testingimportassert_almost_equal# 导入 sklearn.metrics 模块中的 log_loss 和 mean_squared_error 函数fromsklearn.metricsimportlog_loss, mean_squared_error# 导入 scipy.special 模块中的 expit 函数,用于测试...
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) # create and fit the LSTM network model = Sequential() model.add(LSTM(4, input_shape=(1, look_back))) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') ...
〇input_gradients(): [TODO] Softmax()的部分w.r.t输入。 .activations.py import numpy as npfrom .core import Diffableclass LeakyReLU(Diffable):def __init__(self, alpha=0.3):super().__init__()self.alpha = alphaself.inputs = Noneself.outputs = Nonedef forward(self, inputs):# TODO...
:param transition_matrix: Squared transition matrix :math:`A` for successive states or a batch of transition matrices. **Example:** .. doctest:: >>> from jax import random >>> from jax import numpy as jnp >>> import numpyro >>> from numpyro import distributions as dist ...
Squared error Bernoulli VAE loss Wasserstein loss with gradient penalty Noise contrastive estimation loss Activations ReLU Tanh Affine Sigmoid Leaky ReLU ELU SELU GELU Exponential Hard Sigmoid Softplus Models Bernoulli variational autoencoder Wasserstein GAN with gradient penalty ...