pca | pca : A Python Package for Principal Component Analysis | Machine Learning library
kandi X-RAY | pca Summary
kandi X-RAY | pca Summary
pca is a python package to perform Principal Component Analysis and to create insightful plots. The core of PCA is build on sklearn functionality to find maximum compatibility when combining with other packages. But this package can do a lot more. Besides the regular pca, it can also perform SparsePCA, and TruncatedSVD. Depending on your input data, the best approach will be choosen.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Fit the PCA model
- Shortellings T2 test
- Explained variance
- Compute outliers T2
- Performs preprocessing
- Transform the fitted data
- Plot a scatter plot
- Get the cartesian coordinates
- Preprocessing step
- Compute the topfeature of the model
- Embed pdf in rst
- Return all files in the given directory
- Write css to rst file
- Imports an example
- Imports example data
- Plot a feature
- Make a 3d scatter plot
- Compute the mean variance of the data
- Fit the model to the input data
- Make a 3D biplot plot of features
- Compute the outliers for a given PCE test
- Import example dataset
- Performs a Fisher - T2 test on the data
- Plot PCA
- Transform the fitted model
- Convert notebook to html
pca Key Features
pca Examples and Code Snippets
Lines of Code : 49
License : No License
Copydef 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
QUESTION
I have a text file that contains abbreviations like so (simplified example):
...ANSWER
Answered 2021-Jun-11 at 10:22Here’s a ‘tidyverse’ solution:
QUESTION
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 12:52When 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.
QUESTION
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 03:14By 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).
QUESTION
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 20:35EDIT: 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:
QUESTION
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 17:37It 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:
QUESTION
For image clustering I was using a piece of code which worked perfectly.
...ANSWER
Answered 2021-Jun-02 at 08:49I switched to TF2 instead of disabling v2 behavior and that has resolved the problem
QUESTION
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 06:32You can extract importance from summary(pca).
QUESTION
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 17:40For 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:
QUESTION
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 09:30using your data I did this:
QUESTION
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 12:14Use the inverse_transform method:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install pca
It is distributed under the MIT license.
Install the latest version from the GitHub source:
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
