/* Load libraries */
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import TruncatedSVD
from scipy.sparse import csr_matrix
from sklearn import datasets
import numpy as np

/* Load the data */
digits = datasets.load_digits()

/* Standardize the feature matrix */
X = StandardScaler().fit_transform(digits.data)

/* Make sparse matrix */
X_sparse = csr_matrix(X)

/* Create and run an TSVD with one less than number of features */
tsvd = TruncatedSVD(n_components=X_sparse.shape[1]-1)
X_tsvd = tsvd.fit(X)

/* List of explained variances */
tsvd_var_ratios = tsvd.explained_variance_ratio_

/* Create a function */
def select_n_components(var_ratio, goal_var: float) -> int:

    /* Set initial variance explained so far /*
    total_variance = 0.0
    
    /* Set initial number of features */
    n_components = 0
    
    /* For the explained variance of each feature: /*
    for explained_variance in var_ratio:
        
        /* Add the explained variance to the total */
        total_variance += explained_variance
        
        /* Add one to the number of components */
        n_components += 1
        
        /* If we reach our goal level of explained variance */
        if total_variance >= goal_var:
            /* End the loop */
            break
            
    /* Return the number of components */
    return n_components

/* Run function */
select_n_components(tsvd_var_ratios, 0.95)