It is a supervised learning problem so we have some real data to build the model from: Linear regression algorithms: There are many ways to find the coefficients and the intercept, you can use least squares or one of the optimisation methods like gradient decent In this post we will use le...
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
importnumpyasnpimportpandasaspdfromsklearn.model_selectionimporttrain_test_splitfromsklearn.linear_modelimportLinearRegressionimportpickle# 创建示例数据X=np.random.rand(100,1)*10y=2.5*X+np.random.randn(100,1)# 数据分割X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_...
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...
# -*- coding: utf-8 -*- import matplotlib.pyplot as plt import numpy as np class LinearRegression(object): def __init__(self): self._history_w = [] self._cost = [] def load_input_data(self, data_file): with open(data_file) as f: input_x = [] input_y = [] for line ...
首先建立linear_regression.py文件,用于实现线性回归的类文件,包含了线性回归内部的核心函数: # -*- coding: utf-8 -*- import numpy as np class LinerRegression(object): def __init__(self, learning_rate=0.01, max_iter=100, seed=None):
02_python_linear_regression importnumpyasnp#NumPy, a popular library for scientific computing importmatplotlib.pyplotasplt#Matplotlib, a popular library for plotting data plt.style.use('./deeplearning.mplstyle') # x_train is the input variable (size in 1000 square feet)...
我们的目标和单变量线性回归问题中一样,是要找出使得代价函数最小的一系列参数。多变量线性回归的批量梯度下降算法为: 求导数后得到: (3)向量化计算 向量化计算可以加快计算速度,怎么转化为向量化计算呢? 在多变量情况下,损失函数可以写为: 对theta求导后得到: ...
Linear Regression线性回归虽然看上去简单,但是其是最重要的数学模型之一,其他很多模型都建立在它的基础之上。 Linear Regression的表达式子如下: 1 2 3 4 y = Ax + B. A = slope of curve B = bias (point that intersect y-axis) 在本次例子中使用一组汽车价格和销量数据来进行模拟研究。
import numpy as np import pandas as pd import matplotlib.pyplot as plt SEED = 1024 NUM_SAMPLES = 50 # Generate synthetic data def generate_data(num_samples): """Generate dummy data for linear regression.""" X = np.array(range(num_samples)) random_noise = np.random.uniform(-10, 20,...