def plot3D(show=True):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# ax = fig.gca(projection='3d')
predicted = sess.run(inference(X), feed_dict={X: train_X})
x = np.squeeze(np.asarray(train_X1))
y = np.squeeze(np.asarray(train_X2))
z = np.squeeze(np.asarray(train_Y))
z1 = np.squeeze(np.asarray(predicted))
ax.plot(x, y, zs=z, c='b', label='Original')
ax.plot(x, y, zs=z1, c='r', label='Fit')
# ax.scatter(train_X1, train_X2, train_Y, c='y', marker='s')
# ax.scatter(train_X1, train_X2, predicted, c='r', marker='v')
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('Y')
ax.legend()
buf = io.BytesIO()
if show:
# If want to view image in Tensorboard, do not show plot => Strange
# bug!!!
plt.show()
else:
# plt.savefig("/tmp/test.png", format='png')
plt.savefig(buf, format='png')
buf.seek(0)
return buf
def sympletic_integrator_step(dt: float, bodies: list, pairs: list, ax: Axes3D,
*args, **kwargs):
"""A sympletic integrator to update the positions
of bodies in our system of bodies.
Reference
---------
[1]: Sympletic Integrator on Wikipedia
https://en.wikipedia.org/wiki/Symplectic_integrator
"""
for (([x1, y1, z1], v1, m1), ([x2, y2, z2], v2, m2)) in pairs:
dx = x1 - x2
dy = y1 - y2
dz = z1 - z2
mag = dt * ((dx * dx + dy * dy + dz * dz)**(-1.5))
b1m = m1 * mag
b2m = m2 * mag
v1[0] -= dx * b2m
v1[1] -= dy * b2m
v1[2] -= dz * b2m
v2[0] += dx * b1m
v2[1] += dy * b1m
v2[2] += dz * b1m
for (r, [vx, vy, vz], m) in bodies:
r[0] += dt * vx
r[1] += dt * vy
r[2] += dt * vz
ax.clear()
ax.axis("off")
ax.set_xlim(-20., 20.)
ax.set_ylim(-20., 20.)
ax.set_zlim(-20., 20.)
for ii in range(360):
ax.view_init(elev=90., azim=ii)
for name, body in BODIES.items():
pos, _, mass = body
sc = ax.scatter(*pos, s=20 * (mass**0.3), label=name)
ax.legend()
return sc
def plot_loss(self, X, Y, loss, show=True):
# Graphic display
title = str(self.step) + ': Epochs:' + str(self.epochs) + \
" batch:" + str(self.batch_size) + \
" alpha:" + str(self.learning_rate)
train_X1 = X[:, 0]
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.text2D(0.05, 0.95, title, transform=ax.transAxes)
# trisurf plot
# ax.plot_trisurf(np.ravel(train_X1),
# np.ravel(train_X2), np.ravel(Y), color='b')
ax.plot_trisurf(np.ravel(train_X1), np.ravel(Y), np.ravel(loss))
ax.set_xlabel('Weight')
ax.set_ylabel('bias')
ax.set_zlabel('Loss')
ax.legend()
# save image to buffer
buf = io.BytesIO()
if show:
# If want to view image in Tensorboard, do not show plot => Strange
# bug!!!
image_path = self.logs_path + "/images"
if not os.path.exists(image_path):
os.mkdir(image_path)
filename = image_path + "/" + \
"loss-" + \
time.strftime("%Y%m%d-%H%M%S", time.localtime()) + ".png"
plt.savefig(filename, format='png')
plt.show()
else:
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# plt.clf()
return buf
def plot(self, show=True):
# Graphic display
predicted = self.sess.run(self.inference(reuse=True),
feed_dict={self.X: self.input_X})
title = 'Epochs:' + str(self.epochs) + \
" batch:" + str(self.batch_size) + \
" layers:" + str(self.n_layers) + \
" neurons:" + str(self.n_neurals) + \
" alpha:" + str(self.learning_rate)
if self.n_features == 1: # plot 2D image
plt.title(title)
# plt.plot(self.train_X, self.train_Y, c='b', label='Original data')
# plt.plot(self.train_X, predicted, c='r', label='Fitted')
plt.scatter(self.input_X,
self.label_Y,
c='b',
label='Original data')
plt.scatter(self.input_X, predicted, c='r', label='Fitted')
plt.legend()
else: # plot 3D for X1, X2
if (self.n_features == 2):
train_X1, train_X2 = np.hsplit(self.input_X, self.n_features)
else:
train_X1, train_X2, _ = np.hsplit(self.input_X,
self.n_features)
fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
ax = fig.gca(projection='3d')
ax.text2D(0.05, 0.95, title, transform=ax.transAxes)
x = np.squeeze(np.asarray(train_X1))
y = np.squeeze(np.asarray(train_X2))
z = np.squeeze(np.asarray(self.label_Y))
z1 = np.squeeze(np.asarray(predicted))
# ax.plot(x, y, zs=z, c='b', label='Original data')
# ax.plot(x, y, zs=z1, c='r', label='Fitted')
ax.scatter(train_X1,
train_X2,
self.label_Y,
c='b',
marker='s',
label='Original data')
ax.scatter(train_X1,
train_X2,
predicted,
c='r',
marker='v',
label='Fitted')
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('Y')
ax.legend()
# save image to buffer
buf = io.BytesIO()
if show:
# If want to view image in Tensorboard, do not show plot => Strange
# bug!!!
image_path = self.logs_path + "/images"
if not os.path.exists(image_path):
os.mkdir(image_path)
filename = image_path + "/" + \
time.strftime("%Y%m%d-%H%M%S", time.localtime()) + ".png"
plt.savefig(filename, format='png')
plt.show()
else:
plt.savefig(buf, format='png')
buf.seek(0)
plt.close('all')
return buf
plt.title('I hate datasets line graph')
plt.xlabel('County Name')
plt.ylabel('Average Upload number')
plt.tight_layout(pad=0.5)
plt.show()
################### 3d graph implementation
#creating graph based on min, max, avg
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#ax.scatter(y,z,w,color='blue',marker='o')
for (k, v) in enumerate(y):
ax.scatter(y[k], z[k], w[k], marker='o')
#ax.legend([x[k]])
ax.legend(x)
ax.set_title('I hate datasets in 3d')
ax.set_xlabel('min')
ax.set_ylabel('Max')
ax.set_zlabel('Avg')
plt.show()
############## hw 3 portion
#brings in data and sends it into gudhi
distance_matrix = gudhi.read_lower_triangular_matrix_from_csv_file(
csv_file='Maryland_Broadband_Speed_Test__County_Upload.csv')
rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix,
max_edge_length=12.0) #setting parameters
simplex_tree = rips_complex.create_simplex_tree(max_dimension=1)
result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \
repr(simplex_tree.num_simplices()) + ' simplices - ' + \
#%pylab inline
import math
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D as ax
x = np.arange(0, 10, 0.1)
y = np.sin(x)
fig, ax = plt.subplots(facecolor='w', edgecolor='k')
ax.plot(x, y, marker="o", color="r", linestyle='None')
ax.grid(True)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.grid(True)
ax.legend(["y = x**2"])
plt.title('Puntos')
plt.show()
fig.savefig("grafica.png")
def plot(self, X, Y, show=True):
# Graphic display
predicted = self.sess.run(self.predict_model(X))
title = str(self.step) + ': Epochs:' + str(self.epochs) + \
" batch:" + str(self.batch_size) + \
" alpha:" + str(self.learning_rate)
if self.n_features == 1: # plot 2D image
plt.title(title)
# plt.plot(self.train_X, self.train_Y, c='b', label='Original data')
# plt.plot(self.train_X, predicted, c='r', label='Fitted')
plt.scatter(X, Y, c='b', label='Original data')
plt.scatter(X, predicted, c='r', label='Fitted')
plt.legend()
else: # plot 3D for X1, X2
if (self.n_features == 2):
train_X1, train_X2 = np.hsplit(X, self.n_features)
else:
train_X1, train_X2, _ = np.hsplit(X, self.n_features)
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.text2D(0.05, 0.95, title, transform=ax.transAxes)
# line plot
# x = np.squeeze(np.asarray(train_X1))
# y = np.squeeze(np.asarray(train_X2))
# z = np.squeeze(np.asarray(Y))
# z1 = np.squeeze(np.asarray(predicted))
# ax.plot(x, y, zs=z, c='b', label='Original data')
# ax.plot(x, y, zs=z1, c='r', label='Fitted')
# statter plot
ax.scatter(train_X1,
train_X2,
Y,
c='b',
marker='s',
label='Original data')
# ax.scatter(train_X1, train_X2, predicted,
# c='r', marker='v', label='Fitted')
# trisurf plot
# ax.plot_trisurf(np.ravel(train_X1),
# np.ravel(train_X2), np.ravel(Y), color='b')
ax.plot_trisurf(np.ravel(train_X1),
np.ravel(train_X2),
np.ravel(predicted),
color='r')
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('Y')
ax.legend()
# save image to buffer
buf = io.BytesIO()
if show:
# If want to view image in Tensorboard, do not show plot => Strange
# bug!!!
image_path = self.logs_path + "/images"
if not os.path.exists(image_path):
os.mkdir(image_path)
filename = image_path + "/" + \
time.strftime("%Y%m%d-%H%M%S", time.localtime()) + ".png"
plt.savefig(filename, format='png')
plt.show()
else:
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# plt.clf()
return buf
