def DrawDigit(A, label=''):
""" Draw single digit as a greyscale matrix"""
fig = plt.figure(figsize=(6, 6))
# Uso la colormap 'gray' per avere la schacchiera in bianco&nero
img = plt.imshow(A, cmap='gray_r')
plt.xlabel(label)
plt.show()
addComputationIndex=True,
useMultiProcessing=True,
showProgress=True,
)
print("--- %s seconds ---" % (time.time() - start_time))
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
import matplotlib.pyplot as plt
#from matplotlib import cm
#from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
colorMap = plt.cm.get_cmap('jet') #finite element colors
plt.close('all')
fig = plt.figure()
ax = fig.gca(projection='3d')
#reshape output of parametervariation to fit plot_surface
X = np.array(pDict['mass']).reshape((n,n))
Y = np.array(pDict['spring']).reshape((n,n))
Z = np.array(values).reshape((n,n))
surf = ax.plot_surface(X, Y, Z,
cmap=colorMap, linewidth=2,
antialiased=True,
shade = True)
plt.colorbar(surf, shrink=0.5, aspect=5)
plt.tight_layout()
#++++++++++++++++++++++++++++++++++++++++++++++++++
]
# Instantiate a Gaussian Process model
gp = GaussianProcessRegressor(kernel=kernel,
alpha=dy**2,
n_restarts_optimizer=10)
# Fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(X, y)
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, sigma = gp.predict(x, return_std=True)
# Plot the function, the prediction and the 95% confidence interval based on
# the MSE
plt.figure()
plt.errorbar(X.ravel(), y, dy, fmt='r.', markersize=10, label=u'Observations')
plt.plot(x, y_pred, 'b-', label=u'Prediction')
plt.fill(np.concatenate([x, x[::-1]]),
np.concatenate(
[y_pred - 1.9600 * sigma, (y_pred + 1.9600 * sigma)[::-1]]),
alpha=.5,
fc='b',
ec='None',
label='95% confidence interval')
plt.xlabel('$x$')
plt.ylabel('$f(x)$')
plt.ylim(-10, 20)
plt.legend(loc='upper left')
plt.show()
from mpl_toolkits.mplot3d import axes3d
import matplotlib.plt as plt
import numpy as np
fig = plt.figure(5)
ax = fig.gca(projection='3d')
# axes range
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
# axes labels
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
# normalizing a matrix
def normalize(A):
return A / np.linalg.norm(A)
# the vectors from the diagram
V = -normalize(np.array([1.0**0.5, 0.0**0.5, -1.0**0.5]))
L = np.array([0.5**0.5, 0.5**0.5, 0.0**0.5])
N = np.array([0.0**0.5, 0.5**0.5, 0.0**0.5])
R = np.array([-0.5**0.5, 0.0**0.5, 0.5**0.5])
#R = normalize(2*np.dot(N,L)*N-L)
vectors = [