From c74b1e3cd2bdf9f65746edca6bcbbf0554c848ec 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 17:04:39 +0800
Subject: [PATCH] Delete Book4_Ch21_Python_Codes directory

---
 Book4_Ch21_Python_Codes/Bk4_Ch21_01.py        |  26 ----
 Book4_Ch21_Python_Codes/Bk4_Ch21_02.py        | 100 ---------------
 .../Streamlit_Bk4_Ch21_02.py                  | 110 ----------------
 .../Streamlit_Bk4_Ch21_03.py                  | 117 ------------------
 4 files changed, 353 deletions(-)
 delete mode 100644 Book4_Ch21_Python_Codes/Bk4_Ch21_01.py
 delete mode 100644 Book4_Ch21_Python_Codes/Bk4_Ch21_02.py
 delete mode 100644 Book4_Ch21_Python_Codes/Streamlit_Bk4_Ch21_02.py
 delete mode 100644 Book4_Ch21_Python_Codes/Streamlit_Bk4_Ch21_03.py

diff --git a/Book4_Ch21_Python_Codes/Bk4_Ch21_01.py b/Book4_Ch21_Python_Codes/Bk4_Ch21_01.py
deleted file mode 100644
index 3880730..0000000
--- a/Book4_Ch21_Python_Codes/Bk4_Ch21_01.py
+++ /dev/null
@@ -1,26 +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_Ch21_01.py
-
-import numpy as np
-
-def is_pos_def(A):
-    if np.array_equal(A, A.T):
-        try:
-            np.linalg.cholesky(A)
-            return True
-        except np.linalg.LinAlgError:
-            return False
-    else:
-        return False
-
-A = np.array([[1,0],
-              [0,0]])
-
-print(is_pos_def(A))
diff --git a/Book4_Ch21_Python_Codes/Bk4_Ch21_02.py b/Book4_Ch21_Python_Codes/Bk4_Ch21_02.py
deleted file mode 100644
index e0c3ba0..0000000
--- a/Book4_Ch21_Python_Codes/Bk4_Ch21_02.py
+++ /dev/null
@@ -1,100 +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_Ch21_02.py
-
-import sympy
-import numpy as np
-import matplotlib.pyplot as plt
-
-def mesh_circ(c1, c2, r, num):
-    
-    theta = np.arange(0,2*np.pi+np.pi/num,np.pi/num)
-    r     = np.arange(0,r,r/num)
-    theta,r = np.meshgrid(theta,r)
-    xx1 = np.cos(theta)*r + c1
-    xx2 = np.sin(theta)*r + c2
-    
-    return xx1, xx2
-
-
-#define symbolic vars, function
-x1,x2 = sympy.symbols('x1 x2')
-
-A = np.array([[1.5, 0.5],
-              [0.5, 1.5]])
-
-x = np.array([[x1,x2]]).T
-
-f_x = x.T@A@x
-f_x = f_x[0][0]
-print(f_x)
-
-#take the gradient symbolically
-grad_f = [sympy.diff(f_x,var) for var in (x1,x2)]
-print(grad_f)
-
-f_x_fcn = sympy.lambdify([x1,x2],f_x)
-
-#turn into a bivariate lambda for numpy
-grad_fcn = sympy.lambdify([x1,x2],grad_f)
-
-
-xx1, xx2 = mesh_circ(0, 0, 4, 20)
-
-# coarse mesh
-xx1_, xx2_ = mesh_circ(0, 0, 4, 10)
-V = grad_fcn(xx1_,xx2_)
-V_z = np.ones_like(V[1]);
-
-if isinstance(V[1], int):
-    V[1] = np.zeros_like(V[0])
-
-elif isinstance(V[0], int):
-    V[0] = np.zeros_like(V[1])
-
-ff_x = f_x_fcn(xx1,xx2)
-
-color_array = np.sqrt(V[0]**2 + V[1]**2)
-l_3D_vectors = np.sqrt(V[0]**2 + V[1]**2 + V_z**2)
-
-# 3D visualization
-ax = plt.figure().add_subplot(projection='3d')
-ax.plot_wireframe(xx1, xx2, ff_x, rstride=1, 
-                  cstride=1, color = [0.5,0.5,0.5],
-                  linewidth = 0.2)
-ax.contour3D(xx1, xx2, ff_x, 20, cmap = 'RdYlBu_r')
-
-ax.xaxis.set_ticks([])
-ax.yaxis.set_ticks([])
-ax.zaxis.set_ticks([])
-plt.xlim(xx1.min(),xx1.max())
-plt.ylim(xx2.min(),xx2.max())
-ax.set_proj_type('ortho')
-ax.view_init(30, -125)
-ax.set_xlabel('$x_1$')
-ax.set_ylabel('$x_2$')
-ax.set_zlabel('$f(x_1,x_2)$')
-plt.tight_layout()
-
-color_array = np.sqrt(V[0]**2 + V[1]**2)
-
-# 2D visualization
-fig, ax = plt.subplots()
-plt.quiver (xx1_, xx2_, -V[0], -V[1],color_array,
-            angles='xy', scale_units='xy',
-            edgecolor='none', alpha=0.8,cmap = 'RdYlBu_r')
-
-plt.contour(xx1, xx2, ff_x,20, cmap = 'RdYlBu_r')
-plt.show()
-ax.set_aspect('equal')
-ax.xaxis.set_ticks([])
-ax.yaxis.set_ticks([])
-ax.set_xlabel('$x_1$')
-ax.set_ylabel('$x_2$')
-plt.tight_layout()
diff --git a/Book4_Ch21_Python_Codes/Streamlit_Bk4_Ch21_02.py b/Book4_Ch21_Python_Codes/Streamlit_Bk4_Ch21_02.py
deleted file mode 100644
index a78ffd5..0000000
--- a/Book4_Ch21_Python_Codes/Streamlit_Bk4_Ch21_02.py
+++ /dev/null
@@ -1,110 +0,0 @@
-
-###############
-# Authored by Weisheng Jiang
-# Book 4  |  From Basic Arithmetic to Machine Learning
-# Published and copyrighted by Tsinghua University Press
-# Beijing, China, 2022
-###############
-
-
-import streamlit as st
-import plotly.graph_objects as go
-import sympy
-import numpy as np
-
-def bmatrix(a):
-    """Returns a LaTeX bmatrix
-
-    :a: numpy array
-    :returns: LaTeX bmatrix as a string
-    """
-    if len(a.shape) > 2:
-        raise ValueError('bmatrix can at most display two dimensions')
-    lines = str(a).replace('[', '').replace(']', '').splitlines()
-    rv = [r'\begin{bmatrix}']
-    rv += ['  ' + ' & '.join(l.split()) + r'\\' for l in lines]
-    rv +=  [r'\end{bmatrix}']
-    return '\n'.join(rv)
-
-with st.sidebar:
-    
-    st.latex(r'''
-             A = \begin{bmatrix}
-    a & b\\
-    b & c
-    \end{bmatrix}''')
-    
-    st.latex(r'''
-             f(x_1,x_2) = ax_1^2 + 2bx_1x_2 + cx_2^2
-             ''')
-    
-    
-    a = st.slider('a',-2.0, 2.0, step = 0.1)
-    b = st.slider('b',-2.0, 2.0, step = 0.1)  
-    c = st.slider('c',-2.0, 2.0, step = 0.1)  
-    
-#%%
-
-x1_ = np.linspace(-2, 2, 101)
-x2_ = np.linspace(-2, 2, 101)
-
-xx1,xx2 = np.meshgrid(x1_, x2_)
-
-#define symbolic vars, function
-x1,x2 = sympy.symbols('x1 x2')
-
-A = np.array([[a, b],
-              [b, c]])
-
-D,V = np.linalg.eig(A)
-D = np.diag(D)
-
-st.latex(r'''A = \begin{bmatrix}%s & %s\\%s & %s\end{bmatrix}''' %(a, b, b, c))
-st.latex(r'''A = V \Lambda V^{T}''')
-st.latex(bmatrix(A) + '=' + 
-         bmatrix(np.around(V, decimals=3)) + '@' + 
-         bmatrix(np.around(D, decimals=3)) + '@' + 
-         bmatrix(np.around(V.T, decimals=3)))
-
-x = np.array([[x1,x2]]).T
-
-f_x = a*x1**2 + 2*b*x1*x2 + c*x2**2
-
-st.latex(r'''f(x_1,x_2) = ''')
-st.write(f_x)
-
-f_x_fcn = sympy.lambdify([x1,x2],f_x)
-
-ff_x = f_x_fcn(xx1,xx2)
-
-#%% Plot 3D surface
-
-fig_surface = go.Figure(go.Surface(
-    x = x1_,
-    y = x2_,
-    z = ff_x,
-    colorscale= 'RdYlBu_r'))
-
-fig_surface.update_layout(
-    autosize=False,
-    width=500,
-    height=500)
-st.plotly_chart(fig_surface)
-
-#%% Plot 2D contour
-
-fig_contour = go.Figure(
-    go.Contour(
-        z=ff_x,
-        x=x1_,
-        y=x2_,
-        colorscale= 'RdYlBu_r'
-    ))
-
-fig_contour.update_layout(
-    autosize=False,
-    width=500,
-    height=500)
-
-st.plotly_chart(fig_contour)
-
diff --git a/Book4_Ch21_Python_Codes/Streamlit_Bk4_Ch21_03.py b/Book4_Ch21_Python_Codes/Streamlit_Bk4_Ch21_03.py
deleted file mode 100644
index 56f72a4..0000000
--- a/Book4_Ch21_Python_Codes/Streamlit_Bk4_Ch21_03.py
+++ /dev/null
@@ -1,117 +0,0 @@
-
-###############
-# Authored by Weisheng Jiang
-# Book 4  |  From Basic Arithmetic to Machine Learning
-# Published and copyrighted by Tsinghua University Press
-# Beijing, China, 2022
-###############
-
-import numpy as np
-from sympy import lambdify, diff, exp, latex, simplify, symbols
-import numpy as np
-import plotly.figure_factory as ff
-import plotly.graph_objects as go
-import streamlit as st
-
-x1,x2 = symbols('x1 x2')
-num = 301; # number of mesh grids
-x1_array = np.linspace(-3,3,num)
-x2_array = np.linspace(-3,3,num)
-xx1,xx2 = np.meshgrid(x1_array,x2_array)
-
-# f_xy = x*exp(- x**2 - y**2);
-f_x =  3*(1-x1)**2*exp(-(x1**2) - (x2+1)**2)\
-    - 10*(x1/5 - x1**3 - x2**5)*exp(-x1**2-x2**2)\
-    - 1/3*exp(-(x1+1)**2 - x2**2) 
-
-f_x_fcn = lambdify([x1,x2],f_x)
-f_zz = f_x_fcn(xx1,xx2)
-
-st.latex('f(x_1, x_2) = ' + latex(f_x))
-
-#%% gradient
-
-#take the gradient symbolically
-grad_f = [diff(f_x,var) for var in (x1,x2)]
-
-#turn into a bivariate lambda for numpy
-grad_fcn = lambdify([x1,x2],grad_f)
-
-x1__ = np.linspace(-3,3,40)
-x2__ = np.linspace(-3,3,40)
-# coarse mesh
-xx1_, xx2_ = np.meshgrid(x1__,x2__)
-V = grad_fcn(xx1_,xx2_)
-
-#%% 
-
-#%% visualizations
-
-fig_surface = go.Figure(go.Surface(
-    x = x1_array,
-    y = x2_array,
-    z = f_zz,
-    showscale=False,
-    colorscale = 'RdYlBu_r'))
-fig_surface.update_layout(
-    autosize=False,
-    width =800,
-    height=600)
-
-st.plotly_chart(fig_surface)  
-
-#%% gradient vector plot
-
-f = ff.create_quiver(xx1_, xx2_, 
-                     V[0], V[1], 
-                     arrow_scale=.1,
-                     scale = 0.03)
-
-f_stream = ff.create_streamline(x1__,x2__, 
-                                V[0], V[1], 
-                                arrow_scale=.1)
-
-trace1 = f.data[0]
-trace3 = f_stream.data[0]
-trace2 = go.Contour(
-    x = x1_array,
-    y = x2_array,
-    z = f_zz,
-    showscale=False,
-    colorscale = 'RdYlBu_r')
-
-data=[trace1,trace2]
-fig = go.FigureWidget(data)
-fig.update_layout(
-    autosize=False,
-    width =800,
-    height=800)
-
-
-fig.add_hline(y=0, line_color = 'black')
-fig.add_vline(x=0, line_color = 'black')
-
-fig.update_xaxes(range=[-2, 2])
-fig.update_yaxes(range=[-2, 2])
-fig.update_coloraxes(showscale=False)
-
-st.plotly_chart(fig)  
-
-#%% streamlit plot
-
-data2=[trace3,trace2]
-fig2 = go.FigureWidget(data2)
-fig2.update_layout(
-    autosize=False,
-    width =800,
-    height=800)
-
-
-fig2.add_hline(y=0, line_color = 'black')
-fig2.add_vline(x=0, line_color = 'black')
-
-fig2.update_xaxes(range=[-2, 2])
-fig2.update_yaxes(range=[-2, 2])
-fig2.update_coloraxes(showscale=False)
-
-st.plotly_chart(fig2)  
\ No newline at end of file