我们复用上篇生成的数据。 importnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltSEED=1024NUM_SAMPLES=50# Generate synthetic datadefgenerate_data(num_samples):"""Generate dummy data for linear regression."""X=np.array(range(num_samples))random_noise=np.random.uniform(-10,20,size=num_samples)y...
5. Pytorch教程:Linear Regression的numpy和Autograd实现, 视频播放量 1181、弹幕量 0、点赞数 36、投硬币枚数 24、收藏人数 23、转发人数 5, 视频作者 饭客帆, 作者简介 微软工程师一枚,相关视频:【吴恩达】2024年公认最好的【LLM大模型】教程!大模型入门到进阶,一套
print(z.shape) print(z.detach().numpy()) class LinearRegression(nn.Module): def __init__(self, input_dim, output_dim): super(LinearRegression, self).__init__() self.fc1 = nn.Linear(input_dim, output_dim) def forward(self, x_in): y_pred = self.fc1(x_in) return y_pred #...
frommatplotlibimportpyplot as plt importnumpy as np importrandom print(torch.__version__) # ### 线性回归模型从零开始的实现 # set input feature number num_inputs=2 # set example number num_examples=1000 # set true weight and bias in order to generate corresponded label true_w=[2,-3.4] ...
Now lets write a function to generate linear regression model based on 2 vectors use aclosureto generate a function dynamically: def getlinear(x,y): def inner(x1): return m * x1 + b m = (len(x) * np.sum(x*y) - np.sum(x) * np.sum(y)) / (len(x)*np.sum(x*x) - np....
Linear regression using the Normal Equation 线性回归中,利用最小二乘法,推导出最优解如下: θ^=(XTX)−1XTy 公式自行推导 python,对着上述公式写代码: importnumpyasnpX=2*np.random.rand(100,1)y=4+3*X+np.random.randn(100,1)X_b=np.c_[np.ones((100,1)),X]# add x0 = 1 to each ins...
linearregression均方差 均值方差模型实例 再上代码。 # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns # Random seed np.random.seed(123) ## NUMBER OF ASSETS n_assets = 4...
The entire program to implement simple linear regression using the sklearn module in Python is as follows. from sklearn.linear_model import LinearRegression import numpy weights=numpy.array([30,40,50,60,70]).reshape(-1, 1) heights=numpy.array([100,123,155,178,221]).reshape(-1, 1) ...
Most of the computational work required to generate the regression line was done by NumPy's polyfit function, which computed the values of m and b in the equation y = mx + b.Next unit: Exercise - Perform Linear Regression with Scikit Learn Continue ...
当采用L1正则化时,则变成了LassoRegresion;当采用L2正则化时,则变成了Ridge Regression;线性回归未采用正则化手段。通常来说,在训练模型时是建议采用正则化手段的,特别是在训练数据的量特别少的时候,若不采用正则化手段,过拟合现象会非常严重。L2正则化相比L1而言会更容易收敛(迭代次数少),但L1可以解决训练数据量...