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我也要
如果一个数是3的N次方，那么3^x=N，其中x是整数，那么x=log10(N)/log10(3)。
class Solution {
public:
bool isPowerOfThree(int n) {
double res=log10(n)/log10(3);
if(abs(res-(int)(res))<0.00000000001)
return 1;
else
return 0;
}
};
LeetCodeView
双击文件夹弹窗“找不到应用程序”，但是右键点击打开可以
此电脑、回收站、c、d、f盘、控制面板、任意文件夹等都没有办法双击打开。 右键中包含“使用filemanager打开”，是由于FileManager卸载残留导致的。 懒人专用附件 下载解压上面附件，双击导入可以清理FileManager软件残留 参考： 双击此电脑出现找不到应用程序，但是右键点击可以-其他-优快云问答https://ask.csdn.net/questions/7631439 电脑上的文件夹双击都提示找不到应用程序，但是右击打开就能打开，该怎么解决？ - 知乎 (z
Leetcode 326:Power of Three
Given an integer, write a function to determine if it is a power of three. Follow up: Could you do it without using any loop / recursion? Credits: Special thanks to @dietpepsi for adding this
手把手教你搭建基于Springboot+Vue的酒店管理系统
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326. Power of Three
Given an integer, write a function to determine if it is a power of three. Follow up: Could you do it without using any loop / recursion? Credits: Special thanks to @dietpepsi for adding this prob
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leetcode——326——Power of Three
Given an integer, write a function to determine if it is a power of three. Follow up: Could you do it without using any loop / recursion? Credits: Special thanks to @dietpepsi for adding this
29、几何形态测量学在蝴蝶翅膀研究中的应用
本博客介绍了几何形态测量学在花斑木蛱蝶（Pararge aegeria）翅膀研究中的应用，探讨了环境因素对蝴蝶翅膀大小和形状的影响。通过选择合适的地标并采用Procrustes分析方法，研究揭示了不同景观类型下翅膀形状的差异及其潜在的适应性意义。文章还讨论了发育可塑性在飞行形态变异中的作用，并比较了几何形态测量学与其他传统方法的优势。最后提出了未来研究方向，包括多因素综合分析、发育机制探索及长期生态监测，为理解蝴蝶的适应性进化提供了重要线索。
leetcode 326. Power of Three
传送门 326. Power of Three My Submissions Question Total Accepted:23021Total Submissions:64515Difficulty:Easy Given an integer, write a function to determine if it is a power o...
RK3568笔记十七：LVGL v8.2移植
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MATLAB 与 STK 互联的两种连接方式详解
批量仿真推荐使用方法一；交互分析推荐使用方法二。可以根据具体任务灵活切换，也可以同时使用两种方式，提升建模与仿真的效率。
Leetcode 326. Power of Three
Given an integer, write a function to determine if it is a power of three. Follow up: Could you do it without using any loop / recursion? Credits: Special thanks to @dietpepsi for adding this probl
STM32F407中文手册
STM32f407中文手册 本参考手册面向应用开发人员，提供有关使用 STM32F405xx/07xx、STM32F415xx/17xx、 STM32F42xxx 和 STM32F43xxx 微控制器存储器与外设的完整信息。STM32F405xx/07xx、STM32F415xx/17xx、STM32F42xxx 和 STM32F43xxx 构成一个微 控制器系列，各产品具有不同的存储器大小、封装和外设。有关订购信息以及器件的机械与电气特性，请参见数据手册。有关 ARM Cortex™-M4F 内核的信息，请参见《Cortex™-M4F 技术参考手册》。
leetcode-326-Power of Three
Given an integer, write a function to determine if it is a power of three. Follow up: Could you do it without using any loop / recursion? Credits: Special thanks to @dietpepsi for adding this
WINCC7.3破解文件
WINCC7.3硬件够破解文件，有需要的下载！WINCC7.3硬件够破解文件，有需要的下载！
Devexpress16.2.6破解工具
Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具
326. Power of Three【E】【81】
Given an integer, write a function to determine if it is a power of three. Follow up: Could you do it without using any loop / recursion? Credits: Special thanks to @dietpepsi for adding this
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
326. LeetCode_Math_Power of Three
326. Power of Three My Submissions QuestionEditorial Solution Total Accepted: 38283 Total Submissions: 104551 Difficulty: Easy Given an integer, write a function to determine if it is a powe...
[leetcode] 326. Power of Three
Given an integer, write a function to determine if it is a power of three. Follow up: Could you do it without using any loop / recursion? check n =? 3^x Solution 1 Idea: divide 3 to see w
leetcode_326. Power of Three
Given an integer, write a function to determine if it is a power of three. Follow up: Could you do it without using any loop / recursion? Credits: Special thanks to @dietpepsi for adding this
see-88. Merge Sorted Array
利用array1多余的位置，从后向前遍历二个数组： class Solution { public: void merge(vector<int>& nums1, int m, vector<int>& nums2, int n) { int total=m+n-1; m--; n--; ...
see-1. Two Sum
python版： class Solution: def twoSum(self, nums, target): """ :type nums: List[int] :type target: int :rtype: List[int] """ ans = list() ...
see-123. Best Time to Buy and Sell Stock III
Say you have an array for which the ith element is the price of a given stock on day i. Design an algorithm to find the maximum profit. You may complete at most two transactions. Note: You may not e...
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