I took 130 frames from 2 videos of 2 people. I wanted to test face recognition with the PCA library and MLP but I don’t understand how I should form the data X and data y. From what I know is X has to be the images and y has to be the labels so ..

#### Category : pca

I’m currently trying to fit a Gaussian Process model to my data and have it predict some days ahead. I have reduced my ~10 features down to just 2 components via PCA in sklearn. So now I have PCA1 and PCA2. This was obtained by performing PCA on the training set (40%). pca = PCA(n_components=2) ..

I am trying to run PCA (Principal component analysis) on GPU. I am using skcuda.linalg.PCA for that purpose, but it’s not working. From their tutorial (https://scikit-cuda.readthedocs.io/en/latest/generated/skcuda.linalg.PCA.html): import pycuda.autoinit import pycuda.gpuarray as gpuarray import numpy as np import skcuda.linalg as linalg from skcuda.linalg import PCA as cuPCA pca = cuPCA(n_components=4) # map the data to 4 ..

I’m working on CTU-13 dataset, which you can see the overview of its distributions in the dataset here. I’m using the 11th scenario of CTU-13 dataset which is (S11.csv) and you can access here. Concerning the synthetic nature of the dataset, I need to understand the top most important features for feature engineering stage. #dataset ..

I wanted to draw 2d biplot using my data set (credit card churn data set). But my diagram includes my target variable also as a feature. How can I remove it? Dataset sample showing the headers I have attached the code I’ve used import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler ..

I have used a data set related to credit card churn and derived PCA information as below. But I hardly have an idea to interpret them. Furthermore, how can I identify factors that contribute most to the customer’s decision of termination of credit card usage? Source: Python..

I’m getting the below error Traceback (most recent call last): File "G:EduMy academicsMSc in CSrd semResearchPython filesimpot.py", line 84, in <module> plt.show(‘Principal Component’) File "C:UsersacerAppDataRoamingPythonPython39site-packagesmatplotlibpyplot.py", line 378, in show return _backend_mod.show(*args, **kwargs) TypeError: show() takes 1 positional argument but 2 were given The code I used is as below loading_scores = pd.Series(pca.components_[0]) sorted_loading_scores = loading_scores.abs().sort_values(ascending ..

This is code that I found to help me understand how PCA works in detail; i am hitting a wall trying to understand the pca.components_.T loadings = pd.DataFrame( data=pca.components_.T * np.sqrt(pca.explained_variance_), columns=[f’PC{i}’ for i in range(1, len(X_train.columns) + 1)], index=X_train.columns ) loadings.head() Source: Python..

I am trying to apply autoencoder for dimensionality reduction. and I wonder how it will be applied on a large dataset? I have tried this code below. I have total of 8 features in my data and i want to reduce it to three 3. I have read in this tutorial https://quantdare.com/dimensionality-reduction-method-through-autoencoders/ that if you ..

This was partly answered by @WhoIsJack but not completely solved given the errors I get. Basically, I’m trying to perform principal component analysis on a rolling window of data. For example, I’d run PCA on the last 200 days in the df, move forward 1 day and do PCA again on the last 200 days. ..

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