• 5星
40KB weixin_51194902 2021-01-08 12:19:19
• 5星
56KB qq_40905804 2021-01-27 19:09:08
• 5星
1.46MB weixin_44573410 2021-03-15 21:57:51
• 5星
1.25MB weixin_44573410 2021-03-13 20:36:25
• 5星
137KB weixin_44573410 2021-03-02 23:27:18
• 5星
4.4MB I520ZYS 2021-02-19 09:53:22
• 5星
435KB weixin_44573410 2021-03-13 20:29:44
• 5星
712KB weixin_44573410 2021-03-01 13:55:37
• 5星
69KB weixin_43474701 2021-08-06 22:35:01
• 5星
451B qq_24896243 2021-06-15 23:47:28
• c语言线性回归计算器 线性回归

199KB czr27haha 2013-11-17 18:44:36
• 2KB oldfar 2018-03-28 20:17:48
• C语言版的线性回归分析函数 线性回归分析

72KB shw98wj 2015-06-02 08:42:45
• c语言线性回归一元回归和多元回归 线性回归 5星
54KB jasonding 2011-05-24 14:52:33
• 线性回归C语言 直线拟合

2KB qq_16188027 2014-06-06 21:42:50
• 线性回归 c语言实现_C ++中的线性回归实现 逻辑回归 机器学习 python

线性回归 c语言实现Linear regression models the relation between an explanatory (independent) variable and a scalar response (dependent) variable by fitting a linear equation. 线性回归通过拟合线性方程...

线性回归 c语言实现

Linear regression models the relation between an explanatory (independent) variable and a scalar response (dependent) variable by fitting a linear equation.

线性回归通过拟合线性方程来对解释性(独立)变量和标量响应(因变量)之间的关系进行建模。

For example, Modeling the weights of Individuals with their heights using a linear equation.

例如，使用线性方程式对个人的体重及其身高进行建模。

Before trying to model the relationship on the observed data, you should first determine whether there is a linear relation between them or not, usually, the scatter plot can be a helpful tool to view the relation between the data.

在尝试对观察到的数据建立关系模型之前，首先应确定它们之间是否存在线性关系，通常，散点图可以成为查看数据之间关系的有用工具。

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is, and a is the intercept (the value of y when x = 0).

线性回归线的方程式为Y = a + bX ，其中X为解释变量， Y为因变量。 线的斜率是， a是截距( x = 0时y的值)。

In this article, We will implement the Simple Linear Regression model. Simple linear regression concerns two-dimensional sample points with one independent variable and one dependent variable and finds a linear function that predicts the dependent variable values as a function of the independent variable.

在本文中，我们将实现简单线性回归模型。 简单线性回归涉及具有一个自变量和一个因变量的二维样本点，并找到一个线性函数，该线性函数可预测因变量作为自变量的函数。

When you perform a simple linear regression (or any other type of regression analysis), you get a line of best fit. The data points usually don’t fall on this regression equation line; they are scattered around.

当执行简单的线性回归(或任何其他类型的回归分析)时，您会得到一条最合适的线。 数据点通常不会掉落 在这个回归方程线上； 他们四处散落。

A residual is a vertical distance between a data point and the regression line. Each data point has one residual. It is positive if it is above the regression line and negative if it is below the regression line. If the regression line passes through the point, the residual at that point is zero.

残差是数据点和回归线之间的垂直距离。 每个数据点都有一个残差。 如果它高于回归线，则为正；如果它低于回归线，则为负。 如果回归线通过该点，则该点的残差为零。

The main problem here is to minimize the total residual error to find the line of best fit, if you need more explanation on the theory behind the following equations, I recommend reading this article:

这里的主要问题是最小化总残留误差以找到最佳拟合线，如果您需要以下方程背后的理论更多解释，我建议您阅读本文：

Without going into details, the equations that we should use are:

在不赘述的情况下，我们应使用的公式为：

Simply we can divide it into the following for simplicity:

为了简单起见，我们可以将其分为以下几类：

Now we can start going through the implementation of Linear Regression

现在我们可以开始执行线性回归

1-计算系数： (1- Calculate the coefficients:)

The first step is to implement the function that calculates the coefficients

第一步是实现计算系数的功能

as the expected format for the equation is Y = a + bX, we need to calculate a and b, according to the mentioned relations.

由于方程的期望格式为Y = a + bX，因此我们需要根据上述关系式计算a和b。

1- calculate the mean for the dependent variable and the mean value for the independent variable.

1-计算因变量的平均值和自变量的平均值。

2- Calculate the SS_XY is the sum of the element-wise multiplication of the dependent variable vector with the independent variable vector.

2-计算SS_XY是因变量矢量与自变量矢量的逐元素相乘之和。

3- Calculate the SS_XX is the sum of the element-wise multiplication of the independent variable vector with itself.

3-计算SS_XX是自变量矢量与其自身的元素相乘的总和。

4- Calculate the B_1 coefficient by dividing the SS_XY over the SS_XX value.

4-通过将SS_XY除以SS_XX值来计算B_1系数。

5- Calculate the B_0 coefficient.

5-计算B_0系数。

2-实施课程： (2- Implementing the Class:)

We need to train only two private variables, which are the coefficients.

我们只需要训练两个私有变量，即系数。

For the Fit API, we need it to take the dataset as a vector of the dependent and independent variables, and then estimate the coefficient based on these vectors and store the learned coefficients into our private variables.

对于Fit API，我们需要它将数据集作为因变量和自变量的向量，然后根据这些向量估计系数并将学习到的系数存储到我们的私有变量中。

The remaining part is to implement the Predict API to take the independent variable value and return the estimated value after applying the Linear regression equation.

剩下的部分是实现Predict API，以采用独立变量值并在应用线性回归方程后返回估计值。

3-示例： (3- Example:)

An example of the usage of the Linear Model, we just implemented.We instantiated a class instance with types of float, fit this model to the independent variable and the dependent variable vectors.

我们刚刚实现了一个使用线性模型的示例，我们实例化了一个类型为float的类实例，使该模型适合自变量和因变量向量。

Then we test the model by predicting the values and showing the result after the model fitting.

然后，我们通过预测值并在模型拟合后显示结果来测试模型。

Please note that for debugging purposes, I moved the b_0 and b_1 to be public.

请注意，出于调试目的，我将b_0和b_1公开。

I have also used the matplotlibcpp to plot the output and compare the predicted values against the original data.

我还使用了matplotlibcpp来绘制输出，并将预测值与原始数据进行比较。

You can find an introduction to how to use the matplotlibcpp in the following article.

您可以在以下文章中找到有关如何使用matplotlibcpp的介绍。

The implementation of Linear regression is simple. Linear Regression is a powerful statistical technique and can be used to generate insights on consumer behavior, understanding business, and factors influencing profitability. Linear regressions can also be used in business to evaluate trends and make estimates or forecasts.

线性回归的实现很简单。 线性回归是一种强大的统计技术，可用于生成有关消费者行为，了解业务以及影响盈利能力的因素的见解。 线性回归还可以用于业务中以评估趋势并做出估计或预测。

本文是该系列的一部分，该系列解决了C ++中机器学习算法的实现，在整个系列中，我们将使用此处提供的Iris数据集。

希望本文对您有用，请在发布本系列的新文章时关注以得到通知。

线性回归 c语言实现

展开全文 weixin_26752765 2020-08-11 09:59:07
• 线性回归学习算法c语言实现Linear Search is basically a sequential search algorithm. 线性搜索基本上是一种顺序搜索算法 。 In this algorithm, the key element is searched in the given input array in ...

线性回归学习算法c语言实现

Linear Search is basically a sequential search algorithm.

线性搜索基本上是一种顺序搜索算法

In this algorithm, the key element is searched in the given input array in sequential order.

在此算法中，键元素按给定顺序在给定的输入数组中搜索。

If the key element is found in the input array, it returns the element.

如果在输入数组中找到key元素，它将返回该元素。

线性搜索算法 (Linear Search Algorithm)

Linear_Search ( Array X, Value i)

Linear_Search（数组X，值i）

• Set j to 1

将j设为1

如果j> n，则跳至步骤7

如果X [j] == i，则跳至步骤6
• Then, increment j by 1 i.e. j = j+1

然后，将j加1，即j = j + 1
• Go back to step 2

返回步骤2
• Display the element i which is found at particular index i, then jump to step 8

显示在特定索引i处找到的元素i，然后跳到步骤8

在输入元素集中找不到显示元素。
• Exit/End

退出/结束

线性搜索的伪代码 (Pseudo Code for Linear Search)

procedure LINEAR_SEARCH (array, key)

for each item in the array
if match element == key
return element's index
end if
end for

end procedure

C语言中线性搜索的实现 (Implementation of Linear Search in C)

• Initially, we need to mention or accept the element to be searched from the user.

首先，我们需要提及或接受用户要搜索的元素。
• Then, we create a for loop and start searching for the element in a sequential fashion.

然后，我们创建一个for循环并开始按顺序搜索元素。
• As soon as the compiler encounters a match i.e. array[element] == key value, return the element along with its position in the array.

编译器遇到匹配项即array [element] ==键值后，立即返回元素及其在数组中的位置。
• If no values are found that match the input, it returns -1.

如果找不到与输入匹配的值，则返回-1。

#include <stdio.h>

int LINEAR_SEARCH(int inp_arr[], int size, int val)
{

for (int i = 0; i < size; i++)
if (inp_arr[i] == val)
return i;
return -1;
}

int main(void)
{
int arr[] = { 10, 20, 30, 40, 50, 100, 0 };
int key = 100;
int size = 10;
int res = LINEAR_SEARCH(arr, size, key);
if (res == -1)
else
printf("Item is present at index %d", res);

return 0;
}

Output:

输出：

Item is present at index 5

线性搜索的时间复杂度(Time Complexity of Linear Search)

The best-case complexity is O(1) if the element is found in the first iteration of the loop.

如果在循环的第一次迭代中找到元素，则最佳情况下的复杂度为O（1）

The worst-case time complexity is O(n), if the search element is found at the end of the array, provided the size of the array is n.

如果在数组的末尾找到搜索元素，则最坏情况下的时间复杂度是O（n） ，前提是数组的大小为n。

结论 (Conclusion)

Thus, in this article, we have understood and implemented Linear Search Algorithm.

因此，在本文中，我们已经理解并实现了线性搜索算法。

线性回归学习算法c语言实现

展开全文 cunchi4221 2020-07-18 05:14:52
• #include #include using namespace std; int i,k; ...cout“通过数据集学的的线性模型为：”“f(x)=”“x+”; double x; while(cin>>x){ cout值为： "; cout预测值为： " *x+b; } }

#include <stdio.h>
#include
using namespace std;
int i,k;
//char *s1,*s2;

void linear(double *data1,int *data2,double &w,double &b)
{
int j,t;
k=0,i=0;
FILE *fp,*fp1;
fp1= fopen(“E:\data\data1.txt”, “r”);
fp = fopen(“E:\data\data2.txt”, “r”);
if(fp == NULL|fp1==NULL)
cout<<“false”;
while(fscanf(fp, “%d”, &data2[i]) != EOF)
i++;
while(fscanf(fp1, “%lf”, &data1[k]) != EOF)
k++;
fclose(fp);fclose(fp1);
cout<<“获取的数据集为：”<<endl;
cout<<“xi”<<" “<<“yi”<<endl;
for(j=0;j<i;j++){
cout<<data1[j]<<” "<<data2[j]<<endl;
}

double sum=0,average;

for(t=0;t<k;t++){
sum+=data1[t];
}
average=sum/j;
double Molecular=0;
for(j=0;j<i;j++){
Molecular+=data2[j]*(data1[j]-average);
}
double sum1=0;
for(j=0;j<i;j++){
sum1+=data1[j]*data1[j];
}
double j1=(double)1/j;
double denominator=sum1-j1*sum*sum;
w=Molecular/denominator;
double sumb=0;
for(j=0;j<i;j++){
sumb+=(data2[j]-w*data1[j]);
}
//cout<<endl<<sumb<<" "<<j1;
b=j1*sumb;

}
int main(){
double data1;int data2;
double w, b;
linear(data1,data2,w,b);
cout<<“通过最小二乘法求得参数：”<<endl<<“w=”<<w<<endl<<“b=”<<b;
cout<<endl<<“通过数据集学的的线性模型为：”<<endl<<“f(x)=”<<w<<“x+”<<b;
double x;
while(cin>>x){
cout<<"x值为： "<<x<<endl;
cout<<"预测值为： " <<w*x+b;
}
}

展开全文 heyjudejjj 2019-06-05 11:23:33
• 简单线性回归—C语言 机器学习 算法 python

简单线性回归应该是最简单的机器学习算法了，在这里主要介绍一下算法主要函数的C语言实现，具体算法原理简单一提，如果要学习，可以自行百度。 算法介绍 模型可以如下表示： y=b0+b1×x y = b_0 + b_1 × x y=b0​...

简单线性回归应该是最简单的机器学习算法了，在这里主要介绍一下算法主要函数的C语言实现，具体算法原理简单一提，如果要学习，可以自行百度。

算法介绍

模型可以如下表示：
y = b 0 + b 1 × x y = b_0 + b_1 × x
训练主要依据以下公式：
B 1 = ∑ i = 1 n ( ( x i − m e a n ( x ) ) × ( y i − m e a n ( y ) ) ) ∑ i = 1 n ( x i − m e a n ( x ) ) 2 B_1 = \frac{\sum_{i=1}^{n}{((x_i - mean(x))×(y_i - mean(y)))}}{\sum_{i=1}^{n}{(x_i - mean(x))^2}}

B 0 = m e a n ( y ) − B 1 × m e a n ( x ) B_0 = mean(y) - B_1 × mean(x)

函数

读取csv

• 以下三个函数分别为获取行数、获取列数、获取文本内容。
double **dataset;
int row,col;

int get_row(char *filename)//获取行数
{
char line;
int i = 0;
FILE* stream = fopen(filename, "r");
while(fgets(line, 1024, stream)){
i++;
}
fclose(stream);
return i;
}

int get_col(char *filename)//获取列数
{
char line;
int i = 0;
FILE* stream = fopen(filename, "r");
fgets(line, 1024, stream);
char* token = strtok(line, ",");
while(token){
token = strtok(NULL, ",");
i++;
}
fclose(stream);
return i;
}

void get_two_dimension(char* line, double** data, char *filename)
{
FILE* stream = fopen(filename, "r");
int i = 0;
while (fgets(line, 1024, stream))//逐行读取
{
int j = 0;
char *tok;
char* tmp = strdup(line);
for (tok = strtok(line, ","); tok && *tok; j++, tok = strtok(NULL, ",\n")){
data[i][j] = atof(tok);//转换成浮点数
}//字符串拆分操作
i++;
free(tmp);
}
fclose(stream);//文件打开后要进行关闭操作
}

EXAMPLE

int main()
{
char filename[] = "data.csv";
char line;
double **data;
int row, col;
row = get_row(filename);
col = get_col(filename);
data = (double **)malloc(row * sizeof(int *));
for (int i = 0; i < row; ++i){
data[i] = (double *)malloc(col * sizeof(double));
}//动态申请二维数组
get_two_dimension(line, data, filename);
printf("row = %d\n", row);
printf("col = %d\n", col);

int i, j;
for(i=0; i<row; i++){
for(j=0; j<col; j++){
printf("%f\t", data[i][j]);
}
printf("\n");
}
}

计算均值

m e a n ( x ) = ∑ i = 1 x i c o u n t ( x ) mean(x) = \frac{\sum_{i=1}{x_i}}{count(x)}

float mean(float* values, int length) {//对一维数组求均值
int i;
float sum = 0.0;
for (i = 0; i < length; i++) {
sum += values[i];
}
float mean = (float)(sum / length);
return mean;
}

计算方差

$$variance = \sum_{i=1}^{n}{(x_i - mean(x))^2}$$

float variance(float* values, float mean, int length) {//这里求的是平方和，没有除以n
float sum = 0.0;
int i;
for (i = 0; i < length; i++) {
sum += (values[i] - mean)*(values[i] - mean);
}
return sum;
}

EXAMPLE

float x={1,2,4,3,5};
printf("%f\n",mean(x, 5));
printf("%f",variance(x,mean(x,5),5));

计算协方差

c o v a r i a n c e = ∑ i = 1 n ( ( x i − m e a n ( x ) ) × ( y i − m e a n ( y ) ) ) covariance = {\sum_{i=1}^{n}{((x_i - mean(x))}}×(y_i - mean(y)))

float covariance(float* x, float mean_x, float* y, float mean_y, int length) {
float cov = 0.0;
int i = 0;
for (i = 0; i < length; i++) {
cov += (x[i] - mean_x)*(y[i] - mean_y);
}
return cov;
}

EXAMPLE

float x={1,2,4,3,5};
float y={1,3,3,2,5};
printf("%f",covariance(x,mean(x,5),y,mean(y,5),5));

估计回归参数

B 1 = c o v a r i a n c e ( x , y ) v a r i a n c e ( x ) B_1 = \frac{covariance(x,y)}{variance(x)}

//由均值方差估计回归系数
// 输入参数：数据、存放系数的数组、数据个数
void coefficients(float** data, float* coef, int length) {
float* x = (float*)malloc(length * sizeof(float));
float* y = (float*)malloc(length * sizeof(float));
int i;
for (i = 0; i < length; i++) {
x[i] = data[i];
y[i] = data[i];
//printf("x[%d]=%f,y[%d]=%f\n",i, x[i],i,y[i]);
}
float x_mean = mean(x, length);
float y_mean = mean(y, length);
//printf("x_mean=%f,y_mean=%f\n", x_mean, y_mean);
coef = covariance(x, x_mean, y, y_mean, length) / variance(x, x_mean, length);
coef = y_mean - coef * x_mean;
for (i = 0; i < 2; i++) {
printf("coef[%d]=%f\n", i, coef[i]);
}
}

展开全文 weixin_45821421 2020-11-24 23:40:00
• 线性回归，最小二乘法 C语言实现 线性拟合的c语言程序

weixin_33219360 2021-05-19 19:57:50
• qq_45607873 2020-11-01 14:17:01
• 一元线性回归方程C语言实现 一元线性回归方程 C语言

qq_40367129 2020-01-11 12:21:25
• 线性回归算法c语言实现 线性回归 4星
1KB RSGISGPS 2008-12-05 18:51:07
• 用C语言实现一个简单的一元线性回归算法 机器学习 深度学习

rh8866 2020-07-18 20:52:11
• weixin_35896299 2021-05-20 09:29:15
• 用C语言实现简单的多元线性回归算法（二） 算法 机器学习 python

rh8866 2020-08-12 22:50:15
• weixin_39580727 2021-05-22 02:45:51
• 5星
11KB kevy8106 2009-12-08 11:33:50
• 62KB weixin_38691703 2020-12-20 22:02:50
• 多元线性回归—C语言 算法 机器学习 python

weixin_45821421 2020-11-24 23:43:28
• 4星
9KB zp373860147 2010-10-27 15:05:50
• 线性回归算法源码分析 数据挖掘 线性回归

qq_42232193 2019-04-14 15:52:20
• 用C语言实现简单的多元线性回归算法(一) 机器学习 算法

rh8866 2020-07-19 22:28:07
• weixin_29972227 2021-05-25 08:45:20
• 《c语言描述的一元线性回归》 一元线性回归

532B qq_17745691 2015-06-01 17:54:34
• 最小二乘法一阶线性拟合二阶曲线拟合的C语言程序实现 线性拟合的c语言程序

weixin_29000729 2021-05-19 19:59:08
• weixin_39914868 2021-05-23 06:12:23  ...

c语言线性回归 c语言 订阅