PCA | Personal Computer Architecture Labs

by edelwud C++ Version: Current License: No License

X-Ray Key Features Code Snippets(1)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | PCA Summary

PCA is a C++ library. PCA has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

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Support

PCA has a low active ecosystem.

It has 5 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

PCA has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of PCA is current.

Quality

PCA has no bugs reported.

Security

PCA has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

PCA 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.

Reuse

PCA releases are not available. You will need to build from source code and install.

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PCA Key Features

No Key Features are available at this moment for PCA.

PCA Examples and Code Snippets

Benchmark pca .

python

Lines of Code : 49

License : No License

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def benchmark_pca():
    Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()
    print("Performing logistic regression...")

    N, D = Xtrain.shape
    Ytrain_ind = np.zeros((N, 10))
    for i in range(N):
        Ytrain_ind[i, Ytrain[i]] = 1

Community Discussions

Trending Discussions on PCA

How to sort lines of text alphabetically based on a part of each line?

Implementation of Principal Component Analysis from Scratch Orients the Data Differently than scikit-learn

Partial results on Weka attribute selection

How to colour code a PCA plot based on the data frame cell names?

homals package for Nonlinear PCA in R: Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent

Change in Keras.applications source code results in error in missing variable from localhost

How to save PCA summary?

Is there a way for sklearn pipeline to train with and without a step during a grid search? I can remove steps but how do i pass this to GridSearchCV?

How to make a profile plot (principal component analysis) in R?

How to map the results of Principal Component Analysis back to the actual features that were fed into the model?

QUESTION

How to sort lines of text alphabetically based on a part of each line?

Asked 2021-Jun-12 at 08:18

I have a text file that contains abbreviations like so (simplified example):

...

ANSWER

Answered 2021-Jun-11 at 10:22

Here’s a ‘tidyverse’ solution:

Source https://stackoverflow.com/questions/67934669

QUESTION

Implementation of Principal Component Analysis from Scratch Orients the Data Differently than scikit-learn

Asked 2021-Jun-11 at 14:09

Based on the guide Implementing PCA in Python, by Sebastian Raschka I am building the PCA algorithm from scratch for my research purpose. The class definition is:

...

ANSWER

Answered 2021-Jun-11 at 12:52

When calculating an eigenvector you may change its sign and the solution will also be a valid one.

So any PCA axis can be reversed and the solution will be valid.

Nevertheless, you may wish to impose a positive correlation of a PCA axis with one of the original variables in the dataset, inverting the axis if needed.

Source https://stackoverflow.com/questions/67932137

QUESTION

Partial results on Weka attribute selection

Asked 2021-Jun-11 at 03:14

When I run PCA in WEKA GUI using "Select Attribute", I dont get a complete results instead a partial results with dots at the end.

0.8205 1 -0.493Capacity at 10th Cycle-0.483Capacity at 5th Cycle-0.473Capacity at 50th Cycle-0.261S [M]in Electrolyte -0.256C wt %...

Is there any way to solve this particular issue ?

...

ANSWER

Answered 2021-Jun-11 at 03:14

By default, a maximum of 5 attribute names are included in the generated names.

If you want all of them, use -1 for the -A option (or maximumAttributeNames property in the GOE).

Source https://stackoverflow.com/questions/67925969

QUESTION

How to colour code a PCA plot based on the data frame cell names?

Asked 2021-Jun-07 at 20:35

data.matrix <- matrix(nrow=100, ncol=10)
colnames(data.matrix) <- c(
  paste("wt", 1:5, sep=""),
  paste("ko", 1:5, sep=""))
rownames(data.matrix) <- paste("gene", 1:100, sep="")
for (i in 1:100) {
  wt.values <- rpois(5, lambda=sample(x=10:1000, size=1))
  ko.values <- rpois(5, lambda=sample(x=10:1000, size=1))
 
  data.matrix[i,] <- c(wt.values, ko.values)
}
head(data.matrix)
dim(data.matrix)

pca <- prcomp(t(data.matrix), scale=TRUE) 

intall.packages("ggplot2")
library(ggplot2)
 
pca.data <- data.frame(Sample=rownames(pca$x),
  X=pca$x[,1],
  Y=pca$x[,2])
pca.data
 
ggplot(data=pca.data, aes(x=X, y=Y, label=Sample)) +
  geom_text() +
  xlab(paste("PC1 - ", pca.var.per[1], "%", sep="")) +
  ylab(paste("PC2 - ", pca.var.per[2], "%", sep="")) +
  theme_bw() +
  ggtitle("My PCA Graph")

...

ANSWER

Answered 2021-Jun-07 at 20:35

EDIT: The question was changed after my initial answer, see the bottom for updated answer.

You can get the second character of Sample with substr(), and then pass that to col. Here is an example:

Source https://stackoverflow.com/questions/67878088

QUESTION

homals package for Nonlinear PCA in R: Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent

Asked 2021-Jun-06 at 17:37

I am trying to implement NLPCA (Nonlinear PCA) on a data set using the homals package in R but I keep on getting the following error message:

Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent

The data set I use can be found in the UCI ML Repository and it's called dat when imported in R: https://archive.ics.uci.edu/ml/datasets/South+German+Credit+%28UPDATE%29

Here is my code (some code is provided once the data set is downloaded):

...

ANSWER

Answered 2021-Jun-06 at 17:37

It seems the error comes from code generating NAs in the homals function, specifically for your data for the number_credits levels, which causes problems with sort(as.numeric((rownames(clist[[i]])))) and the attempt to catch the error, since one of the levels does not give an NA value.

So either you have to modify the homals function to take care of such an edge case, or change problematic factor levels. This might be something to file as a bug report to the package maintainer.

As a work-around in your case you could do something like:

Source https://stackoverflow.com/questions/67848304

QUESTION

Change in Keras.applications source code results in error in missing variable from localhost

Asked 2021-Jun-02 at 08:49

For image clustering I was using a piece of code which worked perfectly.

...

ANSWER

Answered 2021-Jun-02 at 08:49

I switched to TF2 instead of disabling v2 behavior and that has resolved the problem

Source https://stackoverflow.com/questions/67789714

QUESTION

How to save PCA summary?

Asked 2021-May-30 at 19:00

I have used prcomp function to perform PCA of my data. I can save other data like, center, scale, score, rotation in csv using write.csv function but I don't know how to save PCA summary.

Data I used

...

ANSWER

Answered 2021-May-30 at 06:32

You can extract importance from summary(pca).

Source https://stackoverflow.com/questions/67758341

QUESTION

Is there a way for sklearn pipeline to train with and without a step during a grid search? I can remove steps but how do i pass this to GridSearchCV?

Asked 2021-May-27 at 17:40

This got closed the first time I asked it because this question asks something similar. However despite the answers showing how to add/remove from a step from the pipeline, none of them show how this works with GridSearchCV and I'm left wondering what to do with the pipeline that I've removed the step from.

I'd like to train a model using a grid search and test the performance both when PCA is performed first and when PCA is omitted. Is there a way to do this? I'm looking for more than simply setting n_components to the number of input variables.

Currently I define my pipeline like this:

...

ANSWER

Answered 2021-May-27 at 17:40

For this, you can have a look at the user guide where it says under the paragraph for nested parameters:

Individual steps may also be replaced as parameters, and non-final steps may be ignored by setting them to 'passthrough'

In your case, I would define a grid with a list of two dictionaries, one in case the whole pipeline is used, and one where the PCA is omitted:

Source https://stackoverflow.com/questions/67725702

QUESTION

How to make a profile plot (principal component analysis) in R?

Asked 2021-May-27 at 09:33

I'm currently running principal component analysis. For the interpretation I want to create a profile (pattern) plot to visualize the correlation between each principal component and the original variables. Is anyone familiar with a package or code to create this in R? I'm using the prcomp() function in R.

See examples:

https://canadianaudiologist.ca/predicting-speech-perception-from-the-audiogram-and-vice-versa/ https://blogs.sas.com/content/iml/2019/11/04/interpret-graphs-principal-components.html

This is similar data to my db:

...

ANSWER

Answered 2021-May-27 at 09:30

using your data I did this:

Source https://stackoverflow.com/questions/67718659

QUESTION

How to map the results of Principal Component Analysis back to the actual features that were fed into the model?

Asked 2021-May-19 at 08:30

When I run the code below, I see the 'pca.explained_variance_ratio_' and a histogram, which shows the proportion of variance explained for each feature.

...

ANSWER

Answered 2021-May-18 at 12:14

Use the inverse_transform method:

Source https://stackoverflow.com/questions/67585809

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

Vulnerabilities

No vulnerabilities reported

Install PCA

You can download it from GitHub.

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