ipca | Instrumented Principal Components Analysis | Genomics library
kandi X-RAY | ipca Summary
kandi X-RAY | ipca Summary
Instrumented Principal Components Analysis
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Top functions reviewed by kandi - BETA
- Fit Bayesian Algorithm
- Fits a PSF with the PSF
- Fits a panel of the fitted panel
- Estimate Bayesian Information Estimation
- Fit a panel of latent factors
- Estimate the model for the given data
- Prepare panel input
- Builds a portfolio from input data
- Estimate the Alternating Least Squares procedure
- Fits the objective function
- Fixture function for FFT
- Function to fit a PSF portfolio using FFT fitting
- Calculates the Gamma distribution for a given portfolio
- Predict a panel of data
- Compute the goodness of a portfolio
- Predict a panel of the data
- Compute the cross - validation cross validation
- Compute predictions for the model
- Wrapper function for fit_cv
- Compute r2 score
- Predict OOS
- Estimate the weights of a model
- Calculates the weights of the model
- Helper function for fitting a basis function
- Performs the fitting algorithm
- Implements Bayesian Method
- Returns a pandas DataFrame containing the Gamma factors
ipca Key Features
ipca Examples and Code Snippets
Community Discussions
Trending Discussions on ipca
QUESTION
Suppose you have the following list called currency_trading_pairs with the following elements:
ANSWER
Answered 2022-Mar-12 at 23:26I would actually convert currency_trading_pairs to a Series object. Then you can split by / and explode, then use isin, and finally groupby(level=0) + any to produce the perfect mask:
QUESTION
I have the following df, from which every cell in it (except index ones) are string types :
ANSWER
Answered 2022-Mar-12 at 17:40It's actually simpler than you think. Just use pd.to_datetime to convert the times to datetime objects, and then use pd.Series.between
QUESTION
The purpose of this program is to:
- read a dataset: the rows are customers, the columns are products customers buy
- apply Principal Components Analysis to reduce the number of features
- apply k-means to determine the cluster which each customer belongs to
- do steps 1, 2 on a new dataset that has the same structure as the original one, but different values
- apply the k-means model determined at step 3 to the new dataset
The issue is that repeated runs give different results in terms of which cluster a customer belongs to. There must be a bug that I am unable to find. Thanks in advance.
...ANSWER
Answered 2021-Jan-16 at 16:35Both runs are actually giving you the same results. The labels produced by KMeans do not mean anything and are arbitrary values to allow you to understand what cluster your data has been assigned. If you look at the clusters that were built in your example, you can see that they are the same, just called differently once you retrained the model ("0" remained "0", "1" became "2", "2" became "3", "3" became "1").
QUESTION
When I need to plot 2 series on y axis, I don't know if this is the best way. It works with lines, but it does not work with line + bars. If I set y2 kind='bar', this serie disappears. indexesM is my dataframe. This is the code:
ANSWER
Answered 2020-Oct-15 at 04:25I created a composite graph by customizing a sample from the official reference. It uses ax.bar().
QUESTION
I'm trying to use IncrementalPCA from scikit-learn. I really need the incremental version of the algorithm because of the online nature of my application. My code couldn't really be simpler:
...ANSWER
Answered 2020-Jun-24 at 11:23Something is wrong with your data.
Here is an 100% working example using some artificial data (n=2000000 and d=243). To help more, upload a sample of your data that results in the error.
QUESTION
I observe that subsequent runs of the same program deliver different labels for the k-means clusters, although the original features are the same. The program applies a set of transformations to an original dataframe, and then to a new dataframe, the pipeline consisting of -- in this order -- StandardScaler--> PCA --> K-means. The PCA and k-means models determined on the initial data are used for the next dataset. Finally, the program does the inverse transformations so that the centroids are shown in the initial features space. So I am puzzled by the different labels, the relevant function here is k-means .predict()
ANSWER
Answered 2020-May-19 at 09:27Yes, this behavior is expected from k-means due to its random initial cluster seed assignment. Of course there are different ways of assigning initial cluster seeds but by default your implementation uses kmeans++ strategy. (See init from KMeans documentation)
QUESTION
I want to extract a numerical percentage in a string. Here are some cases:
- input: "Bank ABC 123% CDE" -> output: 123.00 (as a float)
- input: "Some random bank IPCA + 12,34%" -> output: 12.34
- input: "Bank1 2,3%" -> output: 2.3
Commas are used solely as separators and there's only one percentage for each string, so the following strings will never occur:
- invalid input: "Bank ABC, 123%"
- invalid input: "Bank ABC 123% and 12,34%"
Currently, I'm using the following script in Python
...ANSWER
Answered 2020-Apr-30 at 08:53Your regex removes spaces, as well as everything else. I think that to find something using regex, the best way is to search for it, using the re library.
We will start by looking for all strings ending with %: '.*%'.
For Bank ABC 123% CDE this will return Bank ABC 123% CDE which, contains space and non-digits.
To improve on that, let's look for numbers with 1 comma or dot at most: \d*[,.]?\d*%, this will return 123% for your input
To wrap things up, let's replace the comma with a dot
QUESTION
I have three collections in MongoDB.
In Drug JSON, using MEDID get its respective CMIDs from Composition JSON. Using that CMIDs get its respective chemical Names from the Chemicals JSON, then stores in one array with their medid, cmid and its name.
I'm using NodeJS. Please help me, thanks in advance.
DRUG collection ...ANSWER
Answered 2020-Feb-08 at 06:11db.drug.aggregate([
{
$lookup:
{
from: "composition",
localField: "medid",
foreignField: "medid",
as: "composition"
}
},
$lookup:
{
from: "chemical",
localField: "composition.cmid",
foreignField: "cmid",
as: "chemical"
}
},
$group: {
_id: "$medid",
cmid: { $first: "$composition.cmid" },
name: { $first: "$chemical.name" }
]);
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You can use ipca 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.
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