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存储当前位置左边的元素乘积left和右边的元素成绩right，那么当前元素的返回值即为left*right；
vector<int> productExceptSelf(vector<int>& nums) {
vector<int> ans(nums.size(),1);
int left=1;
for(int i=nums.size()-2;i>=0;i--)
ans[i]=ans[i+1]*nums[i+1];
for(int i =0;i<nums.size();i++)
{
ans[i]*=left;
left*=nums[i];
}
return ans;
}
LeetCodeView
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leetcode-238. Product of Array Except Self
238. Product of Array Except Self Given an array ofnintegers wheren> 1,nums, return an arrayoutputsuch thatoutput[i]is equal to the product of all the elements ofnumsexceptn...
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leetcode 238: Product of Array Except Self
leetcode 238: Product of Array Except Self python, c++ java
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LeetCode 238： Product of Array Except Self
Given an array of n integers where n > 1, nums, return an array output such that output[i] is equal to the product of all the elements of nums except nums[i]. Solve it without division and in O(n).
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LeetCode 238. Product of Array Except Self 题解
本题是前缀积技巧的经典应用。分别计算前缀积和后缀积空间优化：直接在输出数组中计算不使用除法。
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Leetcode 238. Product of Array Except Self (python)
Leetcode 238. Product of Array Except Self题目解法1：构建左右product数组解法2：动态构建product数组 题目 Given an array nums of n integers where n > 1, return an array output such that output[i] is equal to the product ...
python实现下载和离线安装包环境
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LeetCode238Product of Array Except Self
先上题目：题目大意：给一数组。返回一数组，这个数组对应原数组除了对应数其他数的乘积和。注意为额外的O(n)空间技巧：题目中已经明确把最容易想到的求所有数乘积，然后分别除每一个数的简单做法给否定了，因为这个也太没技术含量了。。。这里要提及一个经常用到的概念，前缀和后缀，这个概念对很多程序有至关重要的意义以后如果要介绍KMP算法，其中就用到了字符串前缀和后缀的匹配。其实前缀和后缀顾名思义我们可以把数组中
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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; 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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")
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leetcode 238. Product of Array Except Self
Given an array nums of n integers where n > 1, return an array output such that output[i] is equal to the product of all the elements of nums except nums[i]. Example: Input: [1,2,3,4] Output: [24...
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LeetCode Top 100 Liked Questions 238. Product of Array Except Self (Java版; Medium)
welcome to my blog LeetCode Top 100 Liked Questions 238. Product of Array Except Self (Java版; Medium) 题目描述 Given an array nums of n integers where n > 1, return an array output such that output[i] ...
[leetcode] 238. Product of Array Except Self
leetcode 求数组元素不包括自身的乘积，要求时间复杂度O(n),不能使用除法运算
LeetCode 238——Product of Array Except Self
Given an arraynumsofnintegers wheren> 1, return an arrayoutputsuch thatoutput[i]is equal to the product of all the elements ofnumsexceptnums[i]. Example: Input:[1,2,3,4] Output:[...
Leetcode 238 Product of Array Except Self
Leetcode 238 Product of Array Except Self 题目描述 Given an array nums of n integers where n > 1, return an array output such that output[i] is equal to the product of all the elements of nums except ...
Java [Leetcode 238]Product of Array Except Self
题目描述： Given an array ofnintegers wheren> 1,nums, return an arrayoutputsuch thatoutput[i]is equal to the product of all the elements ofnumsexceptnums[i]. Solve itwithout division...
Leetcode 238. Product of Array Except Self
题目链接：Product of Array Except Self 题目大意：给定一个数组，要求你求出数组中除了该数之外得数字乘积，要求不能使用除法，且除了输出数组之外不使用任何额外得空间 题目思路：很有意思得一道题目，不考虑其他因素得话，我们只需要计算一次全体乘积，每次做除法即可，但是不能使用除法，所以需要考虑其他方法，很容易想到，可以使用前缀和和后缀和进行计算，对于一个数，只需要计算他前一个数...
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