SVM training including cross-validation.Mainly includes machine learning fuctions： Parameter configuration and button operations.
#Pca column 64 bit code#
The code contains a lot of comments to facilitate readers’ understanding.
csv file.Įach step will instantly display the output data of the current step in the table on the right.
The reading method of the prediction set is the same as that of the training set, so it is necessary to ensure that there is no data in the first row and first column, because this part of the content will be discarded. Import the data set that needs to be predicted then make prediction: The model of the above steps can be saved and imported as file, eliminating the need for repeated steps of retraining Prediction Perform cross-validation according to the optimal parameters, and output the accuracy rates and average accuracy rate of the cross-validation. In multi-class classification, the labels are concentration * 100 (so that the decimal point is removed), and convert them into int type. The labels are 0 and 1 in binary classification. Use grid search to find the optimal parameters and display them. Perform dimensionality reduction according to the number of principal components configured, and output the variance contribution rate of each dimension and their sum. datasets will be directly divided into the original categories by concentration mark. According to the classification threshold, the concentration markers are divided into high and low categories. The first column is the spectral wavelength, which has no specific meaning in our data analysis. The first row is the concentration mark, and it will be divided into high and low categories according to the classification threshold in the binary classification. If this project is useful to you, please give me a star ! Main Function Import Training Set # Rcpp_1.0.7 vctrs_0.3.8 scatterplot3d_0.Raman spectral classification base on PyQt5 # loaded via a namespace (and not attached): # stats graphics grDevices utils datasets methods base # LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C # LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 # BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so Quantitative supplementary variables are not supported Supplementary variables are not supported Specific Multiple Correspondance Analysis Quantitative supplementary variables are ignored Qualitative supplementary variables are ignored It includes plotting functions for many packages including ade4, FactoMineR and base R functions prcomp and princomp įor now on, it is usable the following types of analyses : Analysis The github package explor is useful for exploring data. Library ( factoextra ) res <- list ( p0, p1, p2, p2b, p3, p4 ) names ( res ) = c ( 'svd_scaledData', 'prcomp', 'princomp', 'princomp_cov', 'FactoMineR', 'ade4' ) e <- sapply ( res, get_eig ) # get_eig doesn't work on svd svd.e <- eigSum.svd colnames ( svd.e ) <- names ( e ] ) e <- c ( list (svd = svd.e ), e ) e # $svd
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