algorithm-base | 专门为刚开始刷题的同学准备的算法基地，没有最细只有更细，立志用动画将晦涩难懂的算法说的通俗易懂！ | Learning library

by chefyuan Java Version: Current License: MIT

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algorithm-base is a Java library typically used in Tutorial, Learning, Example Codes, LeetCode applications. algorithm-base has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However algorithm-base build file is not available. You can download it from GitHub.

另外我和几位老哥，给刚开始刷题，但是不知道从哪里开始刷的同学，整理了一份【刷题大纲】可以先按这个顺序刷，刷完之后应该就能入门，当然该仓库的大部分题解也是来自那个大纲。需要的同学可以扫描下方二维码回复【刷题大纲】获取.

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algorithm-base has a medium active ecosystem.
It has 8824 star(s) with 1277 fork(s). There are 171 watchers for this library.
It had no major release in the last 12 months.
There are 14 open issues and 4 have been closed. On average issues are closed in 17 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of algorithm-base is current.

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algorithm-base is licensed under the MIT License. This license is Permissive.
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algorithm-base Key Features

提交的代码必须符合编码规范

必须经过测试，可以在刷题网站上面 AC

符合动画思想

可以对代码进行简写，难懂的地方注意添加注释，因为我们的基地主要是为刚刷题的同学服务，所以就尽量让大家容易理解一些。

algorithm-base Examples and Code Snippets

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Plotting a datable with multiple columns (all 1:7 rows) via ggplot with a single geom_point() using aesthetics to color them differently

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algorithm$algorithm <- 1:nrow(algorithm)

ggplot(algorithm, aes(x=algorithm,y=datasetsizes)) + geom_point(aes(color=algorithm)) + labs(x="N", y="Runtime") + 
  scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')

library(ggplot2)
library(microbenchmark)  
require(dplyr)  
library(data.table)

datasetsizes<-c(10^seq(1,4,by=0.5))
l <- length(datasetsizes)

# make a vector with your different conditions
conds <- c('f1', 'f2')

# initalizing a table from the getgo is much cleaner 
# than doing everything in separate variables
dat <- data.frame(
  datasetsizes = rep(datasetsizes, each = length(conds)), # make replicates for each condition
  cond = rep(NA, l*length(conds))
  )
dat[, c("min", "lq", "mean", "median", "uq", "max")] <- 0
dat$cond <- factor(dat$cond, levels = conds)
head(dat)



for(i in 1:l){ # for the love of god, don't use something as long as 'loopvar' as an iterative 
  # I don't have f1 & f2 so I did what I could...
  s <- summary(microbenchmark(
    "f1" = rpois(datasetsizes[i],10),
    "f2" = {length(rpois(datasetsizes[i],10))}))

  dat[which(dat$datasetsizes == datasetsizes[i]), # select rows of current ds size
      c("cond", "min", "lq", "mean", "median", "uq", "max")] <- s[, !colnames(s)%in%c("neval")]
}

dat <- data.table(dat)
ggplot(dat, aes(x=datasetsizes,y=mean)) + 
  geom_point(aes(color = cond)) + 
  geom_line(aes(color = cond)) + # added to see a clear difference btw conds
  labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + 
  scale_y_continuous(trans = 'log10')

algorithm$algorithm <- 1:nrow(algorithm)

ggplot(algorithm, aes(x=algorithm,y=datasetsizes)) + geom_point(aes(color=algorithm)) + labs(x="N", y="Runtime") + 
  scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')

library(ggplot2)
library(microbenchmark)  
require(dplyr)  
library(data.table)

datasetsizes<-c(10^seq(1,4,by=0.5))
l <- length(datasetsizes)

# make a vector with your different conditions
conds <- c('f1', 'f2')

# initalizing a table from the getgo is much cleaner 
# than doing everything in separate variables
dat <- data.frame(
  datasetsizes = rep(datasetsizes, each = length(conds)), # make replicates for each condition
  cond = rep(NA, l*length(conds))
  )
dat[, c("min", "lq", "mean", "median", "uq", "max")] <- 0
dat$cond <- factor(dat$cond, levels = conds)
head(dat)



for(i in 1:l){ # for the love of god, don't use something as long as 'loopvar' as an iterative 
  # I don't have f1 & f2 so I did what I could...
  s <- summary(microbenchmark(
    "f1" = rpois(datasetsizes[i],10),
    "f2" = {length(rpois(datasetsizes[i],10))}))

  dat[which(dat$datasetsizes == datasetsizes[i]), # select rows of current ds size
      c("cond", "min", "lq", "mean", "median", "uq", "max")] <- s[, !colnames(s)%in%c("neval")]
}

dat <- data.table(dat)
ggplot(dat, aes(x=datasetsizes,y=mean)) + 
  geom_point(aes(color = cond)) + 
  geom_line(aes(color = cond)) + # added to see a clear difference btw conds
  labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + 
  scale_y_continuous(trans = 'log10')

how to add max(or min) fitness halting condition to multiprocessing deap python genetic algorithm

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import multiprocessing

pool = multiprocessing.Pool()
toolbox.register("map", pool.map)

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Community Discussions

Trending Discussions on algorithm-base

Plotting a datable with multiple columns (all 1:7 rows) via ggplot with a single geom_point() using aesthetics to color them differently
how to add max(or min) fitness halting condition to multiprocessing deap python genetic algorithm

Trending Discussions on algorithm-base

QUESTION

Plotting a datable with multiple columns (all 1:7 rows) via ggplot with a single geom_point() using aesthetics to color them differently

Asked 2019-Dec-05 at 10:21

I intend to compare timings between two algorithm-based functions f1,f2 via microbenchmark which work on a rpois simulated dataset with sizes of: [1:7] vector given by 10^seq(1,4,by=0.5) i.e. :

[1]    10.00000    31.62278   100.00000   316.22777  1000.00000  3162.27766 10000.00000

Am working on to plot them as well, with all of the information required from microbenchmark (i.e. min,lq,mean,median,uq and max - yes all of them are required, except for expr and neval). I require this via ggplot on a log-log scale with a single geom_point() and aesthetics with each of the information being of different colours and here is my code for that:

library(ggplot2)
library(microbenchmark)  
require(dplyr)  
library(data.table)

datasetsizes<-c(10^seq(1,4,by=0.5))

f1_min<-integer(length(datasetsizes))
f1_lq<-integer(length(datasetsizes))
f1_mean<-integer(length(datasetsizes))
f1_median<-integer(length(datasetsizes))
f1_uq<-integer(length(datasetsizes))
f1_max<-integer(length(datasetsizes))

f2_min<-integer(length(datasetsizes))
f2_lq<-integer(length(datasetsizes))
f2_mean<-integer(length(datasetsizes))
f2_median<-integer(length(datasetsizes))
f2_uq<-integer(length(datasetsizes))
f2_max<-integer(length(datasetsizes))

for(loopvar in 1:(length(datasetsizes)))
{
  s<-summary(microbenchmark(f1(rpois(datasetsizes[loopvar],10), max.segments=3L),f2(rpois(datasetsizes[loopvar],10), maxSegments=3)))

  f1_min[loopvar]    <- s$min[1]
  f2_min[loopvar]    <- s$min[2]
  f1_lq[loopvar]     <- s$lq[1]
  f2_lq[loopvar]     <- s$lq[2]
  f1_mean[loopvar]   <- s$mean[1]
  f2_mean[loopvar]   <- s$mean[2]
  f1_median[loopvar] <- s$median[1]
  f2_median[loopvar] <- s$median[2]
  f1_uq[loopvar]     <- s$uq[1]
  f2_uq[loopvar]     <- s$uq[2]
  f1_max[loopvar]    <- s$max[1]
  f2_max[loopvar]    <- s$max[2]
}

 algorithm<-data.table(f1_min ,f2_min,
                  f1_lq, f2_lq,
                  f1_mean, f2_mean,
                  f1_median, f2_median,
                  f1_uq, f2_uq,
                  f1_max, cdpa_max, datasetsizes) 

ggplot(algorithm, aes(x=algorithm,y=datasetsizes)) + geom_point(aes(color=algorithm)) + labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')

I debug my code at each step and uptil the assignment of computed values to a datatable by the name of 'algorithm' it works fine. Here are the computed runs which are passed as [1:7]vecs into the data table along with datasetsizes (1:7 as well) at the end:

> algorithm
            f1_min      f2_min          f1_lq       f2_lq          f1_mean     f2_mean     f1_median f2_median                 f1_uq       f2_uq          f1_max      f2_max datasetsizes
1:       86.745000   21.863000     105.080000   23.978000       113.645630   24.898840         113.543500   24.683000     120.243000   25.565500      185.477000   39.141000     10.00000
2:      387.879000   52.893000     451.880000   58.359000       495.963480   66.070390         484.672000   62.061000     518.876500   66.116500      734.149000  110.370000     31.62278
3:     1608.287000  341.335000    1845.951500  382.062000      1963.411800  412.584590        1943.802500  412.739500    2065.103500  443.593500     2611.131000  545.853000    100.00000
4:        5.964166    3.014524       6.863869    3.508541         7.502123    3.847917           7.343956    3.851285       7.849432    4.163704        9.890556    5.096024    316.22777
5:       23.128505   29.687534      25.348581   33.654475        26.860166   37.576444          26.455269   37.080149      28.034113   41.343289       35.305429   51.347386   1000.00000
6:       79.785949  301.548202      88.112824  335.135149        94.248141  370.902821          91.577462  373.456685      98.486816  406.472393      135.355570  463.908240   3162.27766
7:      274.367776 2980.122627     311.613125 3437.044111       337.287131 3829.503738         333.544669 3820.517762     354.347487 4205.737045      546.996092 4746.143252  10000.00000

The microbenchmark computed values fine as expected but the ggplot throws up this error:

Don't know how to automatically pick scale for object of type data.table/data.frame. Defaulting to continuous.
Error: Aesthetics must be either length 1 or the same as the data (7): colour, x

Am not being able to resolve this, can anyone let me know what is possibly wrong and correct the plotting procedure for the same?

Also on a sidenote I had to extract all the values (min,lq,mean,median,uq,max) seperately from the computed benchmark seperately since I cant take that as a datatable from the summary itself since it contained expr (expression) and neval columns. I was able to eliminate one of the columns using

algorithm[,!"expr"] or algorithm[,!"neval"]

but I can't eliminate two of them together, i.e.

algorithm[,!"expr",!"neval"] or algorithm[,!("expr","neval")] or algorithm[,!"expr","neval"] - all possible combinations like that don't work (throws 'invalid argument type' error).

Any possible workaround or solution to this and the plotting (main thing) would be highly appreciated!

ANSWER

Answered 2019-Dec-05 at 10:21

Your problem lies mainly with the fact that you're referring to an algorithm column in the ggplot formula that does not exist in your object.

From what you gave, I could do the following :

algorithm$algorithm <- 1:nrow(algorithm)

ggplot(algorithm, aes(x=algorithm,y=datasetsizes)) + geom_point(aes(color=algorithm)) + labs(x="N", y="Runtime") + 
  scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')

and plot this fine :

EDIT : let's clean this up a bit...

As per OP's request, I've cleaned up his code a bit.

There are a lot of things you can work on to improve on your code's readability, but I'm focusing more on the practical aspect here. Basically, join your variables together in a table if you know they'll end up as such. There are a bunch of tricks you can use to assign the values to the correct spots, a few of which you'll see in the code below.

library(ggplot2)
library(microbenchmark)  
require(dplyr)  
library(data.table)

datasetsizes<-c(10^seq(1,4,by=0.5))
l <- length(datasetsizes)

# make a vector with your different conditions
conds <- c('f1', 'f2')

# initalizing a table from the getgo is much cleaner 
# than doing everything in separate variables
dat <- data.frame(
  datasetsizes = rep(datasetsizes, each = length(conds)), # make replicates for each condition
  cond = rep(NA, l*length(conds))
  )
dat[, c("min", "lq", "mean", "median", "uq", "max")] <- 0
dat$cond <- factor(dat$cond, levels = conds)
head(dat)



for(i in 1:l){ # for the love of god, don't use something as long as 'loopvar' as an iterative 
  # I don't have f1 & f2 so I did what I could...
  s <- summary(microbenchmark(
    "f1" = rpois(datasetsizes[i],10),
    "f2" = {length(rpois(datasetsizes[i],10))}))

  dat[which(dat$datasetsizes == datasetsizes[i]), # select rows of current ds size
      c("cond", "min", "lq", "mean", "median", "uq", "max")] <- s[, !colnames(s)%in%c("neval")]
}

dat <- data.table(dat)
ggplot(dat, aes(x=datasetsizes,y=mean)) + 
  geom_point(aes(color = cond)) + 
  geom_line(aes(color = cond)) + # added to see a clear difference btw conds
  labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + 
  scale_y_continuous(trans = 'log10')

This give the following plot.

Source https://stackoverflow.com/questions/59191112

QUESTION

how to add max(or min) fitness halting condition to multiprocessing deap python genetic algorithm

Asked 2018-Dec-20 at 00:56

this question is a follow-on to answer of this question about python deap genetic algorithm library: How to add elimination mechanism in Python genetic algorithm based on DEAP

using reference code from deap github: https://github.com/DEAP/deap/blob/master/examples/ga/onemax.py

line 112 while max(fits) < 100 and g < 1000: #from onemax.py

on the deap github example 'onemax_mp.py': https://github.com/DEAP/deap/blob/master/examples/ga/onemax_mp.py

how do i add a max(or min) condition similar to max(fits) < 100 in the onemax_mp.py?

if i do add this condition is this condition applied to each process in the entire multi-process pool of processes? if one process meets the end condition are the other processes halted?
right now it seems that i can only control the number of generations:

https://github.com/DEAP/deap/blob/master/examples/ga/onemax_mp.py

line 40

algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats, halloffame=hof) #ngen=40 means calculate 40 generations

i am new to stackoverflow, please let me know if i need to edit this question to fit forum rules

ANSWER

Answered 2018-Dec-20 at 00:56

So, the line you're looking at is the termination condition. The evolution stops when an individual with fitness greater than 100 is found or after 1000 generations. I've done a lot of work with MOEAs, but I'm not too familiar with DEAP. That disclaimer aside, it looks like it's not evolving separate populations, just doing parallelized evaluation. So there's only one population. From the docs, it looks like you could take onemax.py and slot in a multiprocessing pool by doing this:

import multiprocessing

pool = multiprocessing.Pool()
toolbox.register("map", pool.map)

I took this code from here: https://deap.readthedocs.io/en/master/tutorials/basic/part4.html

Source https://stackoverflow.com/questions/53859991

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Vulnerabilities

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Install algorithm-base

You can download it from GitHub.
You can use algorithm-base like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the algorithm-base component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .