AI中常用的代码

在AI领域，有很多常用的代码，本文将介绍一些常用的代码片段，包括Python、C++和Java等语言。

Python

1. 导入常用库

在AI开发中，常用的库包括numpy、pandas、scikit-learn、tensorflow等。

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn import preprocessing

2. 加载数据集

data = pd.read_csv('data.csv')

3. 数据预处理

数据预处理包括标准化、归一化等操作。

scaler = preprocessing.StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

4. 模型训练

在AI中，常用的模型包括线性回归、逻辑回归、决策树、随机森林等。

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)

C++

1. 导入常用库

#include <iostream>
#include <cmath>
#include <iomanip>
#include <vector>
#include <algorithm>

2. 定义向量

typedef vector<double> vec;
typedef vector<vec> mat;

3. 定义矩阵乘法

mat operator*(const mat &a, const mat &b) {
    mat c(a.size(), vec(b[0].size()));
    for (int i = 0; i < a.size(); i++)
        for (int j = 0; j < b[0].size(); j++)
            for (int k = 0; k < b.size(); k++)
                c[i][j] += a[i][k] * b[k][j];
    return c;
}

4. 定义梯度下降函数

double cost(mat &X, vec &Y, vec &W) {
    vec A = X * W;
    vec B = Y - A;
    return B * B / (2 * X.size());
}

vec gradient(mat &X, vec &Y, vec &W) {
    vec g(X[0].size(), 0);
    for (int i = 0; i < X.size(); i++) {
        vec A = X * W;
        vec B = A - Y;
        for (int j = 0; j < X[0].size(); j++)
            g[j] += B[i] * X[i][j] / X.size();
    }
    return g;
}

vec gd(mat &X, vec &Y) {
    vec W(X[0].size(), 0);
    double alpha = 0.01;
    for (int i = 0; i < 1000; i++) {
        W = W - gradient(X, Y, W) * alpha;
        if (i % 100 == 0)
            cout << cost(X, Y, W) << endl;
    }
    return W;
}

Java

1. 导入常用库

import java.util.Arrays;
import java.util.List;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
import java.util.stream.Stream;

2. 创建流

Stream<Integer> stream1 = Stream.of(1, 2, 3, 4, 5);
Stream<String> stream2 = Stream.of("a", "b", "c");
IntStream stream3 = IntStream.range(1, 100);

3. 转换流

List<Integer> list = Arrays.asList(1, 2, 3, 4, 5);
Stream<Integer> stream4 = list.stream();

Stream<String> stream5 = Stream.of("a,b,c", "d,e,f");
Stream<String[]> stream6 = stream5.map(s -> s.split(","));
Stream<String> stream7 = stream6.flatMap(Arrays::stream);

4. 终止操作

List<Integer> list = Arrays.asList(1, 2, 3, 4, 5);
int sum1 = list.stream().reduce(0, Integer::sum);
int sum2 = list.stream().mapToInt(Integer::intValue).sum();
List<Integer> filteredList = list.stream().filter(i -> i % 2 == 0).collect(Collectors.toList());