def mesh_plot(plt, X, Y, Z, title):
plt.plot_surface(X, Y, Z, rstride=1, cstride=1, alpha=0.7, cmap=cm.jet)
# DEBUG plt.plot_wireframe(X, Y, Z, rstride=1, cstride=1)
plt.contourf(X, Y, Z, zdir='z', cmap=cm.jet,
offset=-400) # These used to use coolwarm
plt.contourf(X, Y, Z, zdir='x', cmap=cm.jet, offset=41.6)
plt.contourf(X, Y, Z, zdir='y', cmap=cm.jet, offset=-87.4)
plt.set_xlabel('Lat')
plt.set_xlim(41.6, 42.2)
plt.set_ylabel('Long')
plt.set_ylim(-88.0, -87.4)
plt.set_zlabel('Number of Crimes')
plt.set_zlim(-100, 1000)
plt.set_title(f'{title}')
def surfacePlot(x, t, y, fOut=None, showPlot=True):
X = np.outer(x, np.ones(len(t)))
T = np.outer(np.ones(len(x)), t)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_surface(T, X, y, cmap='viridis', edgecolor='none')
# ax.set_title('Surface plot')
plt.grid(True)
plt.set_xlabel('t')
plt.set_ylabel('x')
plt.set_zlabel('y')
if fOut is not None:
plt.savefig(fOut)
if showPlot:
plt.show()
return fig
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 14 12:02:50 2017
@author: vincentkao
"""
#PCA
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.decomposition import PCA
import numpy as np
iris = datasets.load_iris()
X = iris.data[:, :2]
Y = iris.target
X_reduced = PCA(n_components=3).fit_transform(iris.data)
plt.scatter(X_reduced[Y==0, 0], np.zeros((50,1))+0.02, color='red', marker='^')
plt.scatter(X_reduced[Y==1, 0], np.zeros((50,1))-0.02, color='blue', marker='x')
plt.scatter(X_reduced[Y==2, 0], np.zeros((50,1)), color='green', marker='o')
plt.set_title("First three PCA directions")
plt.set_xlabel("1st eigenvector")
plt.w_xaxis.set_ticklabels([])
plt.set_ylabel("2nd eigenvector")
plt.w_yaxis.set_ticklabels([])
plt.set_zlabel("3rd eigenvector")
plt.w_zaxis.set_ticklabels([])
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x = np.zeros(100)
y = np.zeros(100)
z = np.zeros(100)
z_inc = 4.0 / 99.0
theta_inc = (8.0 * 3.14) / 99.0
for i in range(100):
theta = -4.0 * 3.14 + theta_inc * i
z[i] = -2.0 + z_inc * i
r = z[i] * z[i] + 1
x[i] = (r * sin(theta))
y[i] = (r * cos(theta))
plt.plot(x, y, z, projection='3d')
plt.xlabel("x label")
plt.ylabel("y label")
plt.set_zlabel("z label")
plt.legend()
plt.show()
theta_1 = np.linspace(-1, 4, 100)
cost_values = np.zeros((len(theta_0), len(theta_1)))
for i in range(len(theta_0)):
for j in range(len(theta_1)):
t = np.array([theta_0[i], theta_1[j]])
cost_values[i, j] = cost_function(X, y, t)
# In[64]:
fig = plt.figure(figsize=(12, 8))
ax = fig.gca(projection='3d')
surf = ax.plot_surface(theta_0, theta_1, cost_values, cmap='viridis')
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.xlabel("$\Theta_0$")
plt.ylabel("$\Theta_1$")
plt.set_zlabel("$J(\Theta)$")
# ##
# ### Task 7: Plotting the Convergence
# ---
# Plot $J(\theta)$ against the number of iterations of gradient descent:
# In[67]:
plt.plot(costs)
plt.xlabel("Iterations")
plt.ylabel("$Theta")
#