ARTS-week34

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本文字数： 3.8k | 阅读时长 ≈ 3 分钟

Algorithms

Binary Tree Paths
思路: 递归 

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/**
 * Definition for a binary tree node.
 * struct TreeNode {
 *     int val;
 *     TreeNode *left;
 *     TreeNode *right;
 *     TreeNode(int x) : val(x), left(NULL), right(NULL) {}
 * };
 */
class Solution {
public:
    vector<string> binaryTreePaths(TreeNode* root) {
        if (!root) {
            return {};
        }
        if (!root->left && !root->right) {
            return {to_string(root->val)};
        }
        
        vector<string> left = binaryTreePaths(root->left);
        vector<string> right = binaryTreePaths(root->right);
        
        left.insert(left.end(), right.begin(), right.end());
        for (auto &i : left) {
            i = to_string(root->val) + "->" + i;
        }
        return left;
    }
};

Add Digits 

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class Solution {
public:
    int addDigits(int num) {
        while(num > 9) {
            num = num / 10 + num % 10;
        }
        return num;
    }
};

看到一个一行搞定的答案: 

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class Solution {
public:
    int addDigits(int num) {
        return (num == 0) ? 0 : (num - 1) % 9 + 1;
    }
};

Review

本周阅读英文文章 Building a Music Recommendation Engine with Probabilistic Matrix Factorization in PyTorch

Technique

使用joblib提供基于磁盘的简单缓存，并且支持非常大的numpy数组，首先安装joblib库: 

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pip install joblib

使用装饰器Memory.cache来存储函数结果: 

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>>> from joblib import Memory
>>> memory = Memory(cachedir="./save")
>>> 
>>> @memory.cache
... def sum_m(a, b):
...     return a + b
... 
>>> print(sum_m(1, 2))
________________________________________________________________________________
[Memory] Calling __main__--home-ubuntu-test-<stdin>.sum_m...
sum_m(1, 2)
____________________________________________________________sum_m - 0.0 s, 0.0 min
3

看一下save的目录结构: 

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$ tree -h
.
└── [4.0K]  joblib
    └── [4.0K]  __main__--home-ubuntu-test-<stdin>
        └── [4.0K]  sum_m
            ├── [4.0K]  0f2b8227db955581b11071a6cf8988a3
            │   ├── [  70]  metadata.json
            │   └── [   5]  output.pkl
            └── [  36]  func_code.py

4 directories, 3 files

当在类中使用时，只需要:

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class Foo(object):

    def __init__(self, args):
        self.method = memory.cache(self.method)

    def method(self, ...):
        pass

Share

使用iperf3测试网络带宽 

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$ git clone https://github.com/esnet/iperf.git
$ cd iperf/
$ ./configure
$ make
$ make install

iperf必须安装在链路的两端: 服务器端和客户端，在安装好之后启动服务端:

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$ iperf3 -s

然后启动客户端连接服务器，生成吞吐量统计: 

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$ iperf3 -c 192.168.1.2
Connecting to host 192.168.1.2, port 5201
[  5] local 192.168.1.6 port 38590 connected to 192.168.1.2 port 5201
[ ID] Interval           Transfer     Bitrate         Retr  Cwnd
[  5]   0.00-1.00   sec   112 KBytes   915 Kbits/sec    3   23.4 KBytes       
[  5]   1.00-2.00   sec   109 KBytes   892 Kbits/sec    0   26.2 KBytes       
[  5]   2.00-3.00   sec   103 KBytes   847 Kbits/sec    3   26.2 KBytes       
[  5]   3.00-4.00   sec   172 KBytes  1.41 Mbits/sec    1   29.0 KBytes       
[  5]   4.00-5.00   sec   127 KBytes  1.04 Mbits/sec    0   31.7 KBytes       
[  5]   5.00-6.00   sec   138 KBytes  1.13 Mbits/sec    0   35.9 KBytes       
[  5]   6.00-7.00   sec   222 KBytes  1.82 Mbits/sec    0   44.1 KBytes       
[  5]   7.00-8.00   sec   101 KBytes   824 Kbits/sec    1   1.38 KBytes       
[  5]   8.00-9.00   sec   171 KBytes  1.40 Mbits/sec   28   27.6 KBytes       
[  5]   9.00-10.00  sec   127 KBytes  1.04 Mbits/sec    1   31.7 KBytes       
- - - - - - - - - - - - - - - - - - - - - - - - -
[ ID] Interval           Transfer     Bitrate         Retr
[  5]   0.00-10.00  sec  1.35 MBytes  1.13 Mbits/sec   37             sender
[  5]   0.00-10.24  sec  1.23 MBytes  1.01 Mbits/sec                  receiver

iperf Done.

还有很多好玩的选项，组合使用很强大。

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