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scurve | drawing space-filling curves | Math library

 by   cortesi Python Version: Current License: No License

 by   cortesi Python Version: Current License: No License

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kandi X-RAY | scurve Summary

scurve is a Python library typically used in Utilities, Math, Unity applications. scurve has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
A collection of algorithms and visualisation tools related to space-filling curves.
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Quality
Security
Security
License
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kandi-support Support

  • scurve has a low active ecosystem.
  • It has 412 star(s) with 46 fork(s). There are 30 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 2 open issues and 1 have been closed. There are 3 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of scurve is current.
scurve Support
Best in #Math
Average in #Math
scurve Support
Best in #Math
Average in #Math

quality kandi Quality

  • scurve has 0 bugs and 0 code smells.
scurve Quality
Best in #Math
Average in #Math
scurve Quality
Best in #Math
Average in #Math

securitySecurity

  • scurve has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • scurve code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
scurve Security
Best in #Math
Average in #Math
scurve Security
Best in #Math
Average in #Math

license License

  • scurve does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
scurve License
Best in #Math
Average in #Math
scurve License
Best in #Math
Average in #Math

buildReuse

  • scurve releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
scurve Reuse
Best in #Math
Average in #Math
scurve Reuse
Best in #Math
Average in #Math
Top functions reviewed by kandi - BETA

kandi has reviewed scurve and discovered the below as its top functions. This is intended to give you an instant insight into scurve implemented functionality, and help decide if they suit your requirements.

  • Print progress bar .
  • Calculate the entropy of a block .
  • Generate a hilbert point .
  • Draws the canvas .
  • Compute the correlation between two triangles .
  • Return the index of elements in p .
  • Compute the Hilbert - polynomial index .
  • Implementation of igraycode .
  • Parse a color .
  • Set the bit value of x .

scurve Key Features

A library for drawing space-filling curves like the Hilbert Curve.

Kmeans clustering in R combined with data.table package

copy iconCopydownload iconDownload
make_bins <- function (dtbl, nbins) {
  # we only need to cluster if there are more unique values for the grid cost than the number of bins desired
  if (dtbl[,length(unique(grid_cost))] > nbins) {
    bins <- kmeans(dtbl[, grid_cost], nbins)$cluster
  } else {
    bins <- dtbl[,dtbl[,as.numeric(factor(grid_cost))]]
  }
  return(bins)
}
m <- mtcars
setDT(m)
m[,grid_cost:=wt]

nbins <- 10
m[, bin := make_bins(.SD, nbins),by=c("cyl")]
m

     mpg cyl  disp  hp drat    wt  qsec vs am gear carb grid_cost bin
 1: 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4     2.620   1
 2: 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4     2.875   3
 3: 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1     2.320   2
 4: 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1     3.215   4
 5: 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2     3.440   7
 6: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1     3.460   6
...
-----------------------
make_bins <- function (dtbl, nbins) {
  # we only need to cluster if there are more unique values for the grid cost than the number of bins desired
  if (dtbl[,length(unique(grid_cost))] > nbins) {
    bins <- kmeans(dtbl[, grid_cost], nbins)$cluster
  } else {
    bins <- dtbl[,dtbl[,as.numeric(factor(grid_cost))]]
  }
  return(bins)
}
m <- mtcars
setDT(m)
m[,grid_cost:=wt]

nbins <- 10
m[, bin := make_bins(.SD, nbins),by=c("cyl")]
m

     mpg cyl  disp  hp drat    wt  qsec vs am gear carb grid_cost bin
 1: 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4     2.620   1
 2: 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4     2.875   3
 3: 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1     2.320   2
 4: 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1     3.215   4
 5: 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2     3.440   7
 6: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1     3.460   6
...

Community Discussions

Trending Discussions on scurve
  • Kmeans clustering in R combined with data.table package
Trending Discussions on scurve

QUESTION

Kmeans clustering in R combined with data.table package

Asked 2021-Apr-24 at 21:01

I have a dataset that I wish to do kmeans cluster on.

Basically there're many ReEDSregion, and within each ReEDSregion there're many classes (ReEDSregion 1, class 1; ReEDSregion 1, class 2; ReEDSregion 2, class 1, etc.).

I want to do kmeans clustering with 10 clusters for the variable "grid_cost" for each ReEDSregion and class combination (by ReEDSregion and class).
Some ReEDSregion and class combo have less than 10 observations and hence will have as many clusters as the number of observations. I first created a function for that:

make_bins <- function (dtbl, nbins) {
  # we only need to cluster if there are more unique values for the grid cost than the number of bins desired
  if (dtbl[,length(unique(grid_cost))] > nbins) {
    bins <- kmeans(dtbl[, grid_cost], nbins)$cluster
  } else {
    unique_vals <- sort(unique(dtbl[, grid_cost]))
    bins <- dtbl[,which(unique_vals==grid_cost)]
  }
  return(bins)
}

And then I use data table to call the function:

nbins <- 10
scurve[, bin := make_bins(.SD, nbins), by=c("ReEDSregion", "class")]

But it shows error:

Error in [.data.table(scurve, , :=(bin, make_bins(.SD, nbins)), by = c("ReEDSregion", :
Supplied 3 items to be assigned to group 8 of size 7 in column 'bin'. The RHS length must either be 1 (single values are ok) or match the LHS length exactly. If you wish to 'recycle' the RHS please use rep() explicitly to make this intent clear to readers of your code.

What does this mean? can someone help me?

The version of my data table is 1.12.2.

ANSWER

Answered 2021-Apr-24 at 21:01

The error is due to the else part in your code, when you have less different values than bins.

You could use factor in this situation:

make_bins <- function (dtbl, nbins) {
  # we only need to cluster if there are more unique values for the grid cost than the number of bins desired
  if (dtbl[,length(unique(grid_cost))] > nbins) {
    bins <- kmeans(dtbl[, grid_cost], nbins)$cluster
  } else {
    bins <- dtbl[,dtbl[,as.numeric(factor(grid_cost))]]
  }
  return(bins)
}

Testing with mtcars:

m <- mtcars
setDT(m)
m[,grid_cost:=wt]

nbins <- 10
m[, bin := make_bins(.SD, nbins),by=c("cyl")]
m

     mpg cyl  disp  hp drat    wt  qsec vs am gear carb grid_cost bin
 1: 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4     2.620   1
 2: 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4     2.875   3
 3: 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1     2.320   2
 4: 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1     3.215   4
 5: 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2     3.440   7
 6: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1     3.460   6
...

Source https://stackoverflow.com/questions/67246412

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install scurve

You can download it from GitHub.
You can use scurve like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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 .

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