def graph(ynew, h): # функция графика
# Интервал изменения переменной по оси X
xmin = a
xmax = b
dx = 0.01 # Шаг между точками
# список координат по оиси X на отрезке [-xmin; xmax]
xlist = pylab.frange(xmin, xmax, h)
xlist2 = pylab.frange(xmin, xmax, dx)
# значение функции в заданных точках
ylist = ynew # построенная
ylist2 = [real(x) for x in xlist2] # реальная
# рисуем графики
pylab.plot(xlist, ylist)
pylab.plot(xlist2, ylist2)
plt.grid(True)
pylab.show() # Покажем график
def linear(data, kwargs='for_cv'):
"""
Linear interpolation of points set
:param data: List of X, Y
:param kwargs: Impacts on returning value.
If 'for_cv, then returns linear function for given data.
If 'for_plot', then returns standard triple for PLOT function.
Default: 'for_cv'.
:return: Initial points set, X, Y(X)
"""
x, y = data
x_min = min(x)
x_max = max(x)
points = [x, y]
x = array([[1, x[i][0]] for i in range(len(x))])
beta = find_betas(x, y)
if kwargs == 'for_cv':
def func(arg):
return beta[0][0] + beta[1][0] * arg
return func
elif kwargs == 'for_plot':
abscissa = frange(x_min, x_max, 0.01)
ordinate = [beta[0][0] + beta[1][0] * arg for arg in abscissa]
return points, abscissa, ordinate
def demo():
"""Demonstrates solution for exercise: example of usage"""
print "IterateLogistic Demo"
print "Close plot to continue"
print " Creating Bifurcation Diagram"
BifurcationDiagram(logistic_map, 0.1, 500, 128, pylab.frange(0,
1,
npts=600))
def plotposterior(cls, samples, prior_pdf, name, xmin, xmax):
xs = mlab.frange(xmin, xmax, (xmax - xmin) / 100)
ys = [prior_pdf(x) for x in xs]
plt.plot(xs, ys, label='Prior Dist')
plt.hist(samples, bins=30, normed=True, label='Posterior Dist')
plt.title('Prior and Posterior of {}'.format(name))
plt.ylim(ymin=0)
plt.xlim(xmin, xmax)
plt.show()
def plot_posterior_hack(samples, prior_pdf, name, xmin, xmax):
xs = mlab.frange(xmin, xmax, (xmax - xmin) / 100.0)
ys = [prior_pdf(x) for x in xs]
ts = stats.beta(3, 3)
plt.plot(xs, ys, label='Prior Dist')
plt.plot(xs, ts.pdf(xs))
plt.hist(samples, bins=100, normed=True, label='Posterior Dist')
plt.title('Prior and Posterior of {}'.format(name))
plt.ylim(ymin=0)
plt.xlim(xmin, xmax)
plt.show()
def non_linear(data, functions, kwargs='for_cv'):
"""
Non-linear interpolation of points set
:param data: List of X, Y
:param functions: List of functions
:param kwargs: Impacts on returning value.
If 'for_cv, then returns polynomial function for given data.
If 'for_plot', then returns standard triple for PLOT function
Default: 'for_cv'
:return: Initial points set, X, Y(X)
"""
x, y = data
x_min = min(x)
x_max = max(x)
points = [x, y]
# Making a Vandermonde's matrix of functions
functions.insert(0, lambda arg: arg)
functions.insert(0, lambda arg: 1)
x_temp = [list() for _ in range(len(x))]
for i in range(len(x)):
for j in range(len(functions)):
x_temp[i].append(functions[j](x[i][0]))
x = array(x_temp)
del x_temp
beta = find_betas(x, y)
if kwargs == 'for_cv':
def func(arg):
value = 0
for j in range(len(functions)):
value += beta[j][0] * functions[j](arg)
return value
return func
elif kwargs == 'for_plot':
abscissa = frange(x_min, x_max, 0.01)
ordinate = list()
for arg in abscissa:
value = 0
for j in range(len(functions)):
value += beta[j][0] * functions[j](arg)
ordinate.append(value)
return points, abscissa, ordinate
def polynomial(data, k, kwargs='for_cv'):
"""
Polynomial interpolation of points set
:param data: List of X, Y
:param k: Degree of polynomial
:param kwargs: Impacts on returning value.
If 'for_cv, then returns polynomial function for given data.
If 'for_plot', then returns standard triple for PLOT function.
Default: 'for_cv'.
:return: Initial points set, X, Y(X)
"""
x, y = data
x_min = min(x)
x_max = max(x)
points = [x, y]
# Making a Vandermonde's matrix
x_temp = [list() for _ in range(len(x))]
for i in range(len(x)):
for j in range(k + 1):
x_temp[i].append(x[i][0] ** j)
x = array(x_temp)
del x_temp
beta = find_betas(x, y)
if kwargs == 'for_cv':
def func(arg):
value = 0
for j in range(k + 1):
value += beta[j][0] * (arg ** j)
return value
return func
elif kwargs == 'for_plot':
abscissa = frange(x_min, x_max, 0.01)
ordinate = list()
for arg in abscissa:
value = 0
for j in range(k + 1):
value += beta[j][0] * (arg ** j)
ordinate.append(value)
return points, abscissa, ordinate
from __future__ import print_function
import sys
import matplotlib
matplotlib.use("TkAgg")
from matplotlib import pyplot as plt
from matplotlib import pylab as pl
import numpy as np
xval = [x for x in pl.frange(-3.0, 3.0, 0.1)]
if (len(sys.argv) == 2):
option = sys.argv[1]
if (int(option) == 1):
yval = [x for x in xval]
elif (int(option) == 2):
yval = [x**2 for x in xval]
elif (int(option) == 3):
yval = [x**3 for x in xval]
elif (int(option) == 4):
yval = [np.sin(x) for x in xval]
elif (int(option) == 5):
yval = [np.cos(x) for x in xval]
elif (int(option) == 6):
yval = [np.tan(x) for x in xval]
elif (int(option) == 7):
yval = [np.exp(x) for x in xval]
elif (int(option) == 8):
yval = [np.sqrt(abs(x)) for x in xval]
else:
print(""""
Wrong argument list!!.
plt.title("Dot Plot by frequency")
plt.plot(y_, x_, 'ro')
plt.xlabel('Count')
plt.ylabel('# PResidential Request')
# min and max limits
plt.xlim(min(y_)-1, max(y_)+1)
plt.subplot(312)
plt.title("Simple dot plot")
plt.xlabel("# Presedential Request")
plt.ylabel("Frequency")
# prepare data for dot plot
for key, value in x_freq.items():
x__ = np.repeat(key, value)
y__ = frange(0.1, (value/10.0), 0.1)
try:
plt.plot(x__, y__, 'go')
except ValueError:
print x__.shape, y__.shape
# set min and max
plt.ylim(0.0, 0.4)
plt.xlim(xmin=-1)
plt.xticks(x_freq.keys())
plt.subplot(313)
x_vals = []
x_labels = []
y_vals = []
x_tick = 1
for k,v in x_group.items():
#
plt.subplot(311)
plt.plot(y_, x_, 'ro')
plt.title("Dot Plot by Frequency")
plt.xlabel('Count')
plt.ylabel('# Presedential Request')
plt.xlim(min(y_) - 1, max(y_) + 1)
plt.subplot(312)
plt.title("Simple dot plot")
plt.xlabel('# Presedential Request')
plt.ylabel('Frequency')
for key, value in x_freq.items():
x__ = np.repeat(key, value)
y__ = frange(0.1, (value / 10.0), 0.1)
try:
plt.plot(x__, y__, 'go')
except ValueError:
print x__.shape, y__.shape
#
plt.ylim(0.0, 0.4)
plt.xlim(xmin=-1)
plt.xticks(x_freq.keys())
plt.subplot(313)
x_vals = []
x_labels = []
y_vals = []
x_tick = 1
for k, v in x_group.items():
def demo():
"""Demonstrates solution for exercise: example of usage"""
print "IterateLogistic Demo"
print "Close plot to continue"
print " Creating Bifurcation Diagram"
BifurcationDiagram(logistic_map, 0.1, 500, 128, pylab.frange(0,1,npts=600))
def plot(function, min_coords, bound):
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure(figsize=(16, 9))
fig.set_facecolor((0.95, 0.95, 0.95))
fig.canvas.set_window_title('Gradient Descent')
fig.suptitle('Gradient Descent', fontsize=28)
ax = fig.gca()
ax.set_axis_bgcolor(fig.get_facecolor())
ax.tick_params(axis='x', colors=(0.15, 0.15, 0.15))
ax.tick_params(axis='y', colors=(0.15, 0.15, 0.15))
x_min, x_max = bound[0], bound[1]
x = np.linspace(x_min, x_max, 100)
colors = ['red', 'green', 'yellow', 'cyan']
i = 0
n = len(min_coords[0][0])
if n == 2:
ax = fig.gca(projection='3d')
ax.tick_params(axis='z', colors=(0.15, 0.15, 0.15))
ax.set_axis_bgcolor(fig.get_facecolor())
y_min, y_max = x_min, x_max
y = np.linspace(y_min, y_max, 100)
z = function([x, y], n)
ax.plot(xs=x, ys=y, zs=z)
for coords in min_coords:
xs = coords[0][0]
ys = coords[0][1]
zs = function([coords[0][0], coords[0][1]], n)
label = coords[1]
ax.scatter(xs=xs, ys=ys, zs=zs, label=label, c=colors[i])
i += 1
elif n == 1:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color((0.75, 0.75, 0.75))
ax.spines['bottom'].set_color((0.75, 0.75, 0.75))
ax.tick_params(axis='x', colors=(0.15, 0.15, 0.15))
ax.tick_params(axis='y', colors=(0.15, 0.15, 0.15))
ax.set_axis_bgcolor(fig.get_facecolor())
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
plt.xlim(bound[0], bound[1])
x = plt.frange(x_min, x_max, 0.01)
y = [function(xi, n) for xi in x]
ax.plot(x, y, alpha=0.85)
for coords in min_coords:
ax.scatter(coords[0],
function(coords[0], n),
label=coords[1],
c=colors[i],
s=35,
edgecolors='black',
alpha=0.75)
i += 1
else:
return
ax.legend()
plt.show()
