From f985536e822a19050686a92e9a15e75173575720 Mon Sep 17 00:00:00 2001
From: "Iris Series: Visualize Math -- From Arithmetic Basics to Machine
 Learning" <105787223+Visualize-ML@users.noreply.github.com>
Date: Sat, 1 Feb 2025 17:05:00 +0800
Subject: [PATCH] Delete Book4_Ch24_Python_Codes directory

---
 Book4_Ch24_Python_Codes/Bk4_Ch24_01.py | 147 -------------------------
 1 file changed, 147 deletions(-)
 delete mode 100644 Book4_Ch24_Python_Codes/Bk4_Ch24_01.py

diff --git a/Book4_Ch24_Python_Codes/Bk4_Ch24_01.py b/Book4_Ch24_Python_Codes/Bk4_Ch24_01.py
deleted file mode 100644
index 9999597..0000000
--- a/Book4_Ch24_Python_Codes/Bk4_Ch24_01.py
+++ /dev/null
@@ -1,147 +0,0 @@
-
-###############
-# Authored by Weisheng Jiang
-# Book 4  |  From Basic Arithmetic to Machine Learning
-# Published and copyrighted by Tsinghua University Press
-# Beijing, China, 2022
-###############
-
-# Bk4_Ch24_01_A
-
-import numpy as np
-import matplotlib.pyplot as plt 
-import pandas as pd  
-from sklearn.datasets import load_iris
-
-# A copy from Seaborn
-iris = load_iris()
-
-X = iris.data
-y = iris.target
-
-feature_names = ['Sepal length, x1','Sepal width, x2',
-                 'Petal length, x3','Petal width, x4']
-
-# Convert X array to dataframe
-X_df = pd.DataFrame(X, columns=feature_names)
-
-#%% Original data, X
-
-X = X_df.to_numpy();
-
-#%% Gram matrix, G
-
-G = X.T@X
-
-#%% Cosine similarity matrix, C
-
-# from sklearn.metrics.pairwise import cosine_similarity
-# C = cosine_similarity(X)
-from numpy.linalg import inv
-
-S_norm = np.diag(np.sqrt(np.diag(G)))
-# scaling matrix, diagnal element is the norm of x_j
-
-C = inv(S_norm)@G@inv(S_norm)
-
-#%% centroid of data matrix, E(X)
-
-E_X = X_df.mean().to_frame().T
-
-#%% Demean, centralize, X_c
-
-X_c = X_df.sub(X_df.mean())
-
-#%% covariance matrix, Sigma
-
-SIGMA = X_df.cov()
-
-#%% correlation matrix, P
-
-RHO = X_df.corr()
-
-#%% Normalize data, Z_X
-
-from scipy.stats import zscore
-
-Z_X = zscore(X_df)
-
-#%%
-
-# Bk4_Ch24_01_B
-
-#%% QR decomposition
-
-from numpy.linalg import qr
-
-Q, R = qr(X_df,mode = 'reduced')
-
-#%%
-
-# Bk4_Ch24_01_C
-
-#%% Cholesky decomposition
-
-from numpy.linalg import cholesky as chol
-
-L_G = chol(G)
-R_G = L_G.T
-
-#%% Cholesky decompose covariance matrix, SIGMA
-
-L_Sigma = chol(SIGMA)
-
-R_Sigma = L_Sigma.T
-
-#%%
-
-# Bk4_Ch24_01_D
-
-#%% eigen decompose G
-
-from numpy.linalg import eig
-
-Lambs_G,V_G = eig(G)
-Lambs_G = np.diag(Lambs_G)
-
-#%% eigen decompose Sigma, covariance matrix
-
-Lambs_sigma,V_sigma = eig(SIGMA)
-Lambs_sigma = np.diag(Lambs_sigma)
-
-#%% eigen decompose P, correlation matrix
-
-Lambs_P,V_P = eig(RHO)
-Lambs_P = np.diag(Lambs_P)
-
-#%%
-
-# Bk4_Ch24_01_E
-
-#%% SVD, original data X
-
-from numpy.linalg import svd
-
-U_X,S_X_,V_X = svd(X_df, full_matrices=False)
-V_X = V_X.T
-
-# full_matrices=True
-# indices_diagonal = np.diag_indices(4)
-# S_X = np.zeros_like(X_df)
-# S_X[indices_diagonal] = S_X_
-
-# full_matrices=False
-S_X = np.diag(S_X_)
-
-#%% SVD, original data Xc
-
-U_Xc,S_Xc,V_Xc = svd(X_c, full_matrices=False)
-V_Xc = V_Xc.T
-S_Xc = np.diag(S_Xc)
-
-#%% SVD, z scores
-
-U_Z,S_Z,V_Z = svd(Z_X, full_matrices=False)
-V_Z = V_Z.T
-S_Z = np.diag(S_Z)
-