From c9e688146dfc752adeed29e8fd88aa8758cf111d 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 17:04:49 +0800
Subject: [PATCH] Delete Book4_Ch22_Python_Codes directory

---
 Book4_Ch22_Python_Codes/Bk4_Ch22_01.py | 153 -------------------------
 1 file changed, 153 deletions(-)
 delete mode 100644 Book4_Ch22_Python_Codes/Bk4_Ch22_01.py

diff --git a/Book4_Ch22_Python_Codes/Bk4_Ch22_01.py b/Book4_Ch22_Python_Codes/Bk4_Ch22_01.py
deleted file mode 100644
index 1fe31d3..0000000
--- a/Book4_Ch22_Python_Codes/Bk4_Ch22_01.py
+++ /dev/null
@@ -1,153 +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_Ch22_01_A
-
-import numpy as np
-import matplotlib.pyplot as plt 
-import pandas as pd  
-import seaborn as sns 
-from sklearn.datasets import load_iris
-
-# Load the iris data
-iris_sns = sns.load_dataset("iris") 
-# A copy from Seaborn
-iris = load_iris()
-# A copy from Sklearn
-
-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)
-
-#%% Heatmap of X
-
-plt.close('all')
-sns.set_style("ticks")
-
-X = X_df.to_numpy();
-
-# Visualize the heatmap of X
-
-fig, ax = plt.subplots()
-ax = sns.heatmap(X,
-                 cmap='RdYlBu_r',
-                 xticklabels=list(X_df.columns),
-                 cbar_kws={"orientation": "vertical"},
-                 vmin=-1, vmax=9)
-plt.title('X')
-
-#%%
-
-# Bk4_Ch22_01_B
-
-#%% centroid of data matrix, X
-v_1 = np.ones((len(X),1))
-
-E_X = v_1.T@X/len(X)
-# validate: X.mean(axis = 0)
-
-#%% Demean, centralize 
-
-X_demean = X_df.sub(X_df.mean())
-
-
-fig, ax = plt.subplots()
-ax = sns.heatmap(X_demean,
-                 cmap='RdYlBu_r',
-                 xticklabels=list(X_df.columns),
-                 cbar_kws={"orientation": "vertical"},
-                 vmin=-3, vmax=3)
-plt.title('$X_{demean}$')
-
-#%% SSD
-
-SSD = (np.linalg.norm(X - E_X, axis = 1)**2).sum()
-# validate: ((X - E_X)**2).sum()
-# use trace: np.trace((X - E_X).T@(X - E_X))
-
-#%%
-
-# Bk4_Ch22_01_C
-
-# distribution of column features of X
-
-fig, ax = plt.subplots()
-sns.kdeplot(data=X_demean,fill=True, 
-            common_norm=False, 
-            alpha=.3, linewidth=1,
-            palette = "viridis")
-plt.title('Distribution of $X_{demean}$ columns')
-
-#%%
-
-# Bk4_Ch22_01_D
-
-#%% covariance matrix
-
-SIGMA = X_df.cov()
-
-fig, axs = plt.subplots()
-
-h = sns.heatmap(SIGMA,cmap='RdBu_r', linewidths=.05, annot = True)
-h.set_aspect("equal")
-h.set_title('$\Sigma$')
-
-
-#%% correlation matrix
-
-RHO = X_df.corr()
-
-fig, axs = plt.subplots()
-
-h = sns.heatmap(RHO,cmap='RdBu_r', linewidths=.05, annot = True)
-h.set_aspect("equal")
-h.set_title('$\u03A1$')
-
-#%% compare covariance matrices
-
-f,(ax1,ax2,ax3) = plt.subplots(1,3,sharey=True)
-
-g1 = sns.heatmap(X_df[y==0].cov(),cmap="RdYlBu_r",
-                 annot=True,cbar=False,ax=ax1,square=True,
-                 vmax = 0.4, vmin = 0)
-ax1.set_title('Y = 0, setosa')
-
-g2 = sns.heatmap(X_df[y==1].cov(),cmap="RdYlBu_r",
-                 annot=True,cbar=False,ax=ax2,square=True,
-                 vmax = 0.4, vmin = 0)
-ax2.set_title('Y = 1, versicolor')
-
-g3 = sns.heatmap(X_df[y==2].cov(),cmap="RdYlBu_r",
-                 annot=True,cbar=False,ax=ax3,square=True,
-                 vmax = 0.4, vmin = 0)
-ax3.set_title('Y = 2, virginica')
-
-#%% compare correlation matrices
-
-f,(ax1,ax2,ax3) = plt.subplots(1,3,sharey=True)
-
-g1 = sns.heatmap(X_df[y==0].corr(),cmap="RdYlBu_r",
-                 annot=True,cbar=False,ax=ax1,square=True,
-                 vmax = 1, vmin = 0.15)
-ax1.set_title('Y = 0, setosa')
-
-g2 = sns.heatmap(X_df[y==1].corr(),cmap="RdYlBu_r",
-                 annot=True,cbar=False,ax=ax2,square=True,
-                 vmax = 1, vmin = 0.15)
-ax2.set_title('Y = 1, versicolor')
-
-g3 = sns.heatmap(X_df[y==2].corr(),cmap="RdYlBu_r",
-                 annot=True,cbar=False,ax=ax3,square=True,
-                 vmax = 1, vmin = 0.15)
-ax3.set_title('Y = 2, virginica')