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进阶高级测试工程师

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全部文章（共152篇）

题解 | 最小花费爬楼梯

n = int(input()) cost=list(map(int, input().split())) #贪心算和动态规划 dp0 = dp1 =0 for i in range(2,n+1): dp = min(dp0+cost[i-2],dp1+cost[i-1]) dp0...

2025-03-16

0 40

题解 | 斐波那契数列

n = int(input()) a,b =1,1 if n>=3: for i in range(3,n+1): a,b = b,a+b print(b) else: print(1)

2025-03-16

0 40

题解 | 【模板】01背包

n,v = map(int,input().split()) vol = [] weight = [] for _ in range(n): vi, wi = map(int, input().split()) vol.append(vi) weight.append(w...

2025-03-16

0 37

题解 | 最长上升子序列(一)

def func(n, arr): dp = [1] * n for i in range(1, n): for j in range(i): if arr[i] > arr[j]: dp[i] = ma...

2025-03-15

0 33

题解 | 损失函数

import numpy as np def calculate_loss(real_values, predicted_values, delta): mse = np.mean((real_values - predicted_values) ** 2) mae = np.m...

2025-03-04

0 26

题解 | 异常值与缺失值

import numpy as np def preprocess_data(): n=(int)(input()) li=[] for i in range(n): num=float(input()) li.append(num) ...

2025-03-04

0 42

题解 | 特征扩展实现

import numpy as np def feature_scaling(data): # 补全代码 # 标准化 (Z-score 标准化) mean = np.mean(data, axis=0) std = np.std(data, axis=0) ...

2025-03-04

0 32

题解 | 使用梯度下降的线性回归

import numpy as np def linear_regression_gradient_descent(X, y, alpha, iterations): # 补全代码 m, n = X.shape theta = np.zeros((n,1)) # 包括截距项...

2025-03-04

0 41

题解 | 实现压缩行稀疏矩阵（CSR）格式转换

import numpy as np import scipy.sparse def compressed_row_sparse_matrix(dense_matrix): csr_matrix = scipy.sparse.csr_matrix(dense_matrix) vals...

2025-03-03

0 36

题解 | 将向量转换为对角矩阵

import numpy as np def make_diagonal(x): arr = np.array( x , dtype=np.float32 ) return np.diag(arr) if __name__ == "__main__&q...

2025-03-03

0 35