qq_44834369的博客

原创 线性回归

线性回归import torchimport randomclass LinerRegression(): """线性模型 """ def __init__(self, inputDim): """[summary] Args: inputDim (int): 输入维数 """ self.w = torch.normal(0, 0.01, size=(inputDim,1),requir

原创 矩阵求导分子布局

原创 CountingSort

计数排序Java实现排序父类排序模板用于继承Sort.javapackage MYSORT;public class Sort { public static void sort(Comparable[] a, boolean printProcess) {} public static boolean less(Comparable v, Comparable w) { return v.compareTo(w) < 0; } publ

原创 HeapSort

堆排序Java实现排序父类排序模板用于继承Sort.javapackage MYSORT;public class Sort { public static void sort(Comparable[] a, boolean printProcess) {} public static boolean less(Comparable v, Comparable w) { return v.compareTo(w) < 0; } publi

原创 BubbleSort

冒泡排序排序父类排序模板用于继承Sort.javapackage MYSORT;public class Sort { public static void sort(Comparable[] a, boolean printProcess) {} public static boolean less(Comparable v, Comparable w) { return v.compareTo(w) < 0; } public sta

原创 QuickSort

快速排序排序父类排序模板用于继承Sort.javapackage MYSORT;public class Sort { public static void sort(Comparable[] a, boolean printProcess) {} public static boolean less(Comparable v, Comparable w) { return v.compareTo(w) < 0; } public sta

原创 MergeSort

归并排序（自顶向下）排序父类排序模板用于继承Sort.javapackage MYSORT;public class Sort { public static void sort(Comparable[] a, boolean printProcess) {} public static boolean less(Comparable v, Comparable w) { return v.compareTo(w) < 0; } publ

原创 ShellSort

Shell排序Java实现排序父类排序模板用于继承Sort.javapackage MYSORT;public class Sort { public static void sort(Comparable[] a, boolean printProcess) {} public static boolean less(Comparable v, Comparable w) { return v.compareTo(w) < 0; } p

原创 InsertionSort

插入排序Java实现排序父类排序模板用于继承Sort.javapackage MYSORT;public class Sort { public static void sort(Comparable[] a, boolean printProcess) {} public static boolean less(Comparable v, Comparable w) { return v.compareTo(w) < 0; } publ

原创 SelectSort

选择排序Java实现排序父类排序模板用于继承Sort.javapackage MYSORT;public class Sort { public static void sort(Comparable[] a, boolean printProcess) {} public static boolean less(Comparable v, Comparable w) { return v.compareTo(w) < 0; } publ

原创 MyLinerList

线性表Java实现顺序表MyArrayList.javapackage MYLINERLIST;/** * **************************** * * 顺序表 * * **************************** * */import java.util.Iterator;import java.util.NoSuchElementException;public class MyA

原创 SQList

线性表的顺序实现/动态数组(C语言实现)/** * 线性表的顺序实现 * 逻辑关系上相邻的两个元素物理上也相邻 * 优点在于可以随机存取 * 缺点在于插入或删除元素需要移动大量的元素*//*导入必要的头文件*/#include "stdlib.h" //malloc, realloc#include "stdio.h" //printf, scanf/*通用的定义*/#define TR

原创 ArrayStack

LIFO栈（python实现）class Empty(Exception): """Error attempting to access an element from an empty container """ passclass ArrayStack: """LIFO stack """ def __init__(self): self._data = [] def __len__(self):

原创 network

神经网络的实现(前馈运算)python代码import numpy as npfrom activationFunctions import sigmoid, identity_funcclass layer: def __init__(self, inputDim=None, outputDim=None, activation=None): self.inputDim = inputDim self.outputDim = outputDim

原创 activationFunction

激活函数(activation funtions)python 代码import numpy as npfrom matplotlib import pyplot as pltdef step(x): return np.array(x>0, dtype=np.int) def sigmoid(x): return 1/(1+np.exp(-x))def relu(x): return np.maximum(0, x)def plo

原创 mergeSort

Merge Sort(归并排序)伪代码MERGE(A, p, q, r) n1 = q-p+1 n2 = r-q creat L[1..n1+1], R[1..n2+1] for i = 1 to n1 do L[i] = A[p+i-1] for j = 1 to n2 do R[j] = A[q+j] L[n1+1] = +inf R[n2+1] = +inf i, j = 1, 1 for k

原创 Perceptron

感知机实现与门(and)， 与非门(Nand)， 或门(or)， 异或门(xor)原理python 实现class perceptron: @classmethod def AND(cls,x1, x2): w1, w2, b = 0.5, 0.5, -0.7 s = w1*x1+w2*x2+b return 1 if s>0 else 0 @classmethod def NAND(cls, x1, x2):

原创 selectSort

选择排序（python实现）算法原理依次选择最小的元素放到前列首先找出A中最小元素，并将其与A[1]交换，接着找出A中次最小元素，并将其与A[2]交换，以此重复至将A中第n-1小元素放在A[n-1]。伪代码SELECTSORT(A) for j <- 1 to A.length min <- A[j] min_index <- j for i <- j+1 to A.length if A[

原创 insertSort

算法描述（伪代码）代码数据模型class sequence: def __init__(self, list): self.data = [None] + list self.length = len(self.data)-1 def isSorted(self): for i in range(1, self.length-1): if self.data[i] > self.data[i+1]:

原创 logistic_regression

LogisticRegression(LR)模型LR 模型用于二分类问题主要思想为预测positive（y=1）类的后验概率可以理解成对数几率回归损失函数为交叉熵损失函数导入数据import numpy as npfrom sklearn.datasets import load_breast_cancerXy = load_breast_cancer()X = Xy.datay = Xy.targetX.shape,y.shape[0]((569, 30), 569)自己实现