def Spect(
self
): #, dimension): # Dimension is unit type as V(olt) W(att), etc
Axes3D.plot_surface(self.signalx, self.signaly, self.signalz)
if self.signalx.shape == self.signaly.shape == self.signalz.shape:
pass
elif self.signalx.shape == (len(
self.signalx), ) or self.signaly.shape == (len(
self.signaly), ):
# Check for 1D array both
if self.signalx.shape == (len(
self.signalx), ) and self.signaly.shape == (len(
self.signaly), ):
self.signalx, self.signaly = np.meshgrid(
self.signalx, self.signaly)
nx, mx = np.shape(self.signalx)
nz, mz = np.shape(self.signalz)
if nx == nz and mx == mz:
pass
elif nx == mz and mx == nz:
self.signalz = np.rot90(self.signalz, 1)
# find out if it has to be 1 or 3
elif self.signalx.shape == (len(self.signalx), ):
pass
elif self.signaly.shape == (len(self.signaly), ):
pass
if self.signalx.shape == self.signaly.shape != self.signalz.shape:
nx, mx = np.shape(self.signalx)
nz, mz = np.shape(self.signalz)
if nx == nz and mx == mz:
pass
elif nx == mz and mx == nz:
self.signalz = np.rot90(self.signalz, 1)
# find out if it has to be 1 or 3
elif self.signalx.shape == self.signalz.shape != self.signaly.shape:
nx, mx = np.shape(self.signalx)
ny, my = np.shape(self.signalz)
if nx == ny and mx == my:
pass
elif nx == my and mx == ny:
self.signaly = np.rot90(self.signaly, 1)
# find out if it has to be 1 or 3
elif self.signaly.shape == self.signalz.shape != self.signalx.shape:
ny, my = np.shape(self.signaly)
nx, mx = np.shape(self.signalx)
if ny == nx and my == mx:
pass
elif ny == mx and my == nx:
self.signalz = np.rot90(self.signalz, 1)
# find out if it has to be 1 or 3
else:
raise MeasError.DataError(
"No Valid data found in at least one of the axis")
plt.xlabel('time [s]') # Or Sample number
plt.ylabel('Frequency [Hz]') # Or Freq Bins number
plt.zlabel('voltage [mV]') # auto add unit here
plt.title(' ') # set title
plt.grid(True)
plt.show()
def plot3d(matrix, show=True, savefn=None, plt_type="bar", xlabel=None, ylabel=None, zlabel=None, title=None):
"""
Plot the input matrix in 3d. Either save, show, or both.
Supported types are 'bar', 'tri', and 'surface'.
"""
fig = plt.figure()
fig.set_size_inches(18.5, 10.5)
ax = fig.add_subplot(111, projection="3d")
x_data, y_data = np.meshgrid(np.arange(matrix.shape[1]), np.arange(matrix.shape[0]))
x_data = x_data.flatten()
y_data = y_data.flatten()
z_data = matrix.flatten()
if plt_type == "bar":
ax.bar3d(x_data, y_data, np.zeros(len(z_data)), 1, 1, z_data)
elif plt_type == "tri":
ax.plot_trisurf(x_data, y_data, z_data, cmap=cm.jet, linewidth=0.2)
elif plt_type == "surface":
pass
else:
raise RuntimeError("Plot type {} not supported.".format(plt_type))
if xlabel is not None:
plt.ylabel(xlabel)
if ylabel is not None:
plt.xlabel(ylabel)
if zlabel is not None:
plt.zlabel(zlabel)
if title is not None:
plt.title(title)
if savefn is not None:
plt.savefig(savefn, dpi=100)
if show:
plt.show()
return fig
def water(self, *argv): # Dimension is unit type as V(olt) W(att), etc
# http://matplotlib.org/examples/mplot3d/polys3d_demo.html
Axes3D.plot_surface(self.signalx, self.signaly,
self.signalz) # till better funcion
plt.xlabel('time [s]') # Or Sample number
plt.ylabel('Frequency [Hz]') # Or Freq Bins number
plt.zlabel('voltage [mV]') # auto add unit here
plt.title(' ') # set title
plt.grid(True)
plt.show()
def plot_across_companies(tseries, num_features):
for j in range(1, num_features + 1):
for i in range(1, num_features + 1):
if i > j:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for k in tseries.keys():
ax.scatter(tseries[k][:, 0], tseries[k][:, i],
tseries[k][:, j])
plt.xlabel("Number of Days Since 1/1/2016")
plt.ylabel(features[i - 1])
plt.zlabel(features[j - 1])
plt.show()
def plotAgentVel(self, agent_num):
agent = self.dataframe[agent_num]['velocity']
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
Axes3D.scatter(ax,
getWithout(agent[:, 0], -1),
getWithout(agent[:, 1], -1),
zs=np.arange(len(agent))[agent[:, 0] != -1],
s=3)
plt.title("Example Velocity of Agent")
plt.xlabel("x component")
plt.ylabel("y component")
plt.zlabel("Time")
plt.show()
def plotAgentPos(self, agent_num):
agent = self.dataframe[agent_num]['coordinates']
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
Axes3D.scatter(ax,
getWithout(agent[:, 0], 1),
getWithout(agent[:, 1], 1),
zs=np.arange(len(agent))[agent[:, 0] != 1],
s=3)
plt.title("Example Trajectory of Agent")
plt.xlabel("x coordinate")
plt.ylabel("y coordinate")
plt.zlabel("Time")
plt.show()
def plotxyz3d(x, y, z, title=""):
"""
Plot accelerometer readings in 3-D.
(If trouble with "projection='3d'", try: ipython --pylab)
Parameters
----------
x : list or numpy array of floats
y : list or numpy array of floats
z : list or numpy array of floats
title : string
title
Examples
--------
>>> from mhealthx.xio import read_accel_json
>>> #input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/accel_walking_outbound.json.items-6dc4a144-55c3-4e6d-982c-19c7a701ca243282023468470322798.tmp'
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/deviceMotion_walking_outbound.json.items-5981e0a8-6481-41c8-b589-fa207bfd2ab38771455825726024828.tmp'
>>> #input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/deviceMotion_walking_outbound.json.items-a2ab9333-6d63-4676-977a-08591a5d837f5221783798792869048.tmp'
>>> start = 150
>>> device_motion = True
>>> t, axyz, gxyz, uxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)
>>> x, y, z = axyz
>>> title = 'Test vectors'
>>> from mhealthx.utilities import plotxyz3d
>>> plotxyz3d(x, y, z, title)
"""
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection="3d")
# ax.plot(x, y, z) #, marker='o'
ax.plot(x[1::], y[1::], z[1::], label="x, y, z") # , marker='o'
ax.legend()
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
# ax.set_xlim3d(0, 1)
# ax.set_ylim3d(0, 1)
# ax.set_zlim3d(0, 1)
plt.xlabel("x")
plt.ylabel("y")
plt.zlabel("z")
plt.title(title)
plt.show()
def plotxyz3d(x, y, z, title=''):
"""
Plot accelerometer readings in 3-D.
(If trouble with "projection='3d'", try: ipython --pylab)
Parameters
----------
x : list or numpy array of floats
y : list or numpy array of floats
z : list or numpy array of floats
title : string
title
Examples
--------
>>> from mhealthx.xio import read_accel_json
>>> #input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/accel_walking_outbound.json.items-6dc4a144-55c3-4e6d-982c-19c7a701ca243282023468470322798.tmp'
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/deviceMotion_walking_outbound.json.items-5981e0a8-6481-41c8-b589-fa207bfd2ab38771455825726024828.tmp'
>>> #input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/deviceMotion_walking_outbound.json.items-a2ab9333-6d63-4676-977a-08591a5d837f5221783798792869048.tmp'
>>> start = 150
>>> device_motion = True
>>> t, axyz, gxyz, uxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)
>>> x, y, z = axyz
>>> title = 'Test vectors'
>>> from mhealthx.utilities import plotxyz3d
>>> plotxyz3d(x, y, z, title)
"""
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
#ax.plot(x, y, z) #, marker='o'
ax.plot(x[1::], y[1::], z[1::], label='x, y, z') #, marker='o'
ax.legend()
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
#ax.set_xlim3d(0, 1)
#ax.set_ylim3d(0, 1)
#ax.set_zlim3d(0, 1)
plt.xlabel('x')
plt.ylabel('y')
plt.zlabel('z')
plt.title(title)
plt.show()
def kmeans_cluster_plot(X, y, k=3, cmap=plt.cm.Paired, n_dim=2):
kmeans = cluster.KMeans(n_clusters=k).fit(X)
klab = kmeans.labels_
fig = plt.figure()
if n_dim < 3:
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap)
else:
ax = Axes3D(fig)
ax.scatter3D(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=cmap)
plt.title('k-means clusters')
plt.xlabel('PC1')
plt.ylabel('PC2')
if n_dim > 2:
plt.zlabel('PC3')
return fig
def plot3d(matrix,
show=True,
savefn=None,
plt_type='bar',
xlabel=None,
ylabel=None,
zlabel=None,
title=None):
"""
Plot the input matrix in 3d. Either save, show, or both.
Supported types are 'bar', 'tri', and 'surface'.
"""
fig = plt.figure()
fig.set_size_inches(18.5, 10.5)
ax = fig.add_subplot(111, projection='3d')
x_data, y_data = np.meshgrid(np.arange(matrix.shape[1]),
np.arange(matrix.shape[0]))
x_data = x_data.flatten()
y_data = y_data.flatten()
z_data = matrix.flatten()
if plt_type == 'bar':
ax.bar3d(x_data, y_data, np.zeros(len(z_data)), 1, 1, z_data)
elif plt_type == 'tri':
ax.plot_trisurf(x_data, y_data, z_data, cmap=cm.jet, linewidth=.2)
elif plt_type == 'surface':
pass
else:
raise RuntimeError('Plot type {} not supported.'.format(plt_type))
if xlabel is not None:
plt.ylabel(xlabel)
if ylabel is not None:
plt.xlabel(ylabel)
if zlabel is not None:
plt.zlabel(zlabel)
if title is not None:
plt.title(title)
if savefn is not None:
plt.savefig(savefn, dpi=100)
if show:
plt.show()
return fig
def traj(normalized, positions, dimension, time_scale = 10):
lr_asym = np.cos(positions[:, 1] * (np.pi / dimension[1]))
bf_asym_spat = np.cos(positions[:, 2] * (np.pi / dimension[2]))
bf_asym_temp = np.cos(np.linspace(0, np.pi / 2, time_scale))
traj_sym = np.correlate(normalized.sum(axis = 0), np.ones(time_scale))
traj_lr = np.correlate(np.sum(normalized * lr_asym[:, np.newaxis], axis = 0), np.ones(time_scale))
traj_bf = np.correlate(np.sum(normalized * bf_asym_spat[:, np.newaxis], axis = 0), bf_asym_temp)
trajectory = np.c_[traj_bf, traj_lr, traj_sym]
fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
ax.plot(trajectory[:, 0], trajectory[:, 1], trajectory[:, 2])
plt.xlabel('forward/backward')
plt.ylabel('turning')
plt.zlabel('activity')
plt.show()
return trajectory
def plotresults(filename, type=2, xscale='linear', showiter=False,
showtol=True, style=None, limit=False, correct=[], latest=True, label=None):
'''Returns:
if plot requested: handle
otherwise: (par,results,tolret,passes,cputime)
filename = text file with simulation results
type: 1 = BER
2 = SER (as Hamming distance, default)
3 = FER
4 = GSER (as Levenshtein distance)
5 = 3D profile (not supported)
6 = 2D profile (as multiple graphs)
7 = Burst-error profile
8 = passes
9 = cputime
10 = cputime/pass
or a string to match with header
xscale: 'linear' (default) or 'log'
showiter: plot all iterations / indices in set? (default: false = show last)
if true, show all indices
if a list, interpret as indices to show
NOTE: for profile, indices select positions to show results for
showtol: (T1-4,6) plot tolerance limits? (default: true)
style: if absent, do not plot results
if a string, use this as the line format string
if a list, use respectively as color, marker, linestyle
limit: limit number of markers? (default: false)
if true, limits to 20-40 markers per line
if a number 'n', one marker is printed for every 'n' points
correct: data set for BSID parameter correction (default: no correction)
contains: [Ps, Pd, Pi, Reff]
latest: if true (default) load only the latest simulation in file
if false, concatenate all simulations in file
if a list, concatenate the specified simulations in file
label: legend label
Note: types 1 & 2 use the same data set (symbol-error), but just
change the axis labels this facilitates use with binary codes.
'''
# Load data from file, and reorganize as necessary
(par,results,tolerance,passes,cputime,header,comments) = loadresults(filename,latest)
# extract last system and date from comments
system = getafter('Communication System:',comments)
date = getafter('Date:',comments)
# apply correction to BSID parameter if requested
if correct != []:
[Ps, Pd, Pi, Reff] = correct
par = correct_bsid_to_normerror(par, Ps, Pd, Pi, Reff)
# extract results to be plotted
if type==1 or type==2 or type==3 or type==4:
# extract columns according to type requested
tag = { 1: 'BER', 2: 'SER', 3: 'FER', 4:'LD' }
#i = [s.startswith(tag[type]) for s in header]
(results,dd,hh) = get_columns(results,header,tag[type])
(tolerance,dd,hh) = get_columns(tolerance,header,tag[type])
elif type==7:
# compute only P(e_i | e_{i-1}) / P(e_i)
results = (results[:,2] / results[:,3]) / (results[:,1] + results[:,2])
elif not (type >= 1 and type <= 10):
# extract columns according to type requested
(results,dd,hh) = get_columns(results,header,type)
(tolerance,dd,hh) = get_columns(tolerance,header,type)
ns=1
# determine decimation factor to limit number of markers
if limit == True: # limit to 20-40/line
if type==6:
pts = np.size(results,1)
else:
ptr = np.size(par,0)
ns = max(1, pts/20)
elif limit != False: # limit as requested by user
ns = limit
limit = True
# keep results from requested iterations
if isinstance(showiter, list):
results = results[:,showiter]
tolerance = tolerance[:,showiter]
elif not showiter:
# keep last result in set only
results = results[:,-1]
tolerance = tolerance[:,-1]
# keep tolerance values to return
tolret = tolerance
# clear tolerances, if requested
if not showtol:
tolerance = []
# Skip the figure plot if requested
if style is None:
return (par,results,tolret,passes,cputime)
# do the appropriate plot
if type==1 or type==2 or type==3 or type==4:
ylabel = { 1: 'Bit Error Rate', 2: 'Symbol Error Rate', 3: 'Frame Error Rate', 4: 'Generalized Symbol Error Rate' }
h = plotitem(par,results,tolerance,style,xscale,'log',ns,label)
plt.xlabel('Channel Parameter')
plt.ylabel(ylabel[type])
elif type==5:
print "Not supported"
sys.exit(1)
#plotprofile(par,0:(size(results,2)-1),results,tolerance,xscale,'linear','log')
plt.xlabel('Channel Parameter')
plt.ylabel('Value/Position')
plt.zlabel('Symbol Error Rate')
elif type==6:
cols = np.size(results,1)
# convert to numpy arrays and transpose
results = np.array(results).transpose()
tolerance = np.array(tolerance).transpose()
label = '%s, p=%s' % (label, ','.join([ '%g' % n for n in par ]))
h = plotitem(range(cols),results,tolerance,style,'linear','log',ns,label)
plt.xlabel('Value/Position')
plt.ylabel('Symbol Error Rate')
elif type==7:
h = plotitem(par,results,[],style,xscale,'log',ns,label)
plt.xlabel('Channel Parameter')
plt.ylabel(r'Burstiness $Pr[e_i|e_{i-1}] / Pr[e_i]$')
elif type==8:
# Plot the number of samples
h = plotitem(par,passes,[],style,xscale,'log',ns,label)
plt.ylabel('Number of Samples (Frames)')
elif type==9:
# Plot the CPU time used
h = plotitem(par,cputime/3600,[],style,xscale,'log',ns,label)
plt.ylabel('Compute Time Used (Hours)')
elif type==10:
# Plot the CPU time used per sample
h = plotitem(par,cputime/passes,[],style,xscale,'log',ns,label)
plt.ylabel('Compute Time per Sample (Seconds)')
else: # catch-all
h = plotitem(par,results,tolerance,style,xscale,'log',ns,label)
plt.xlabel('Channel Parameter')
plt.ylabel('Value')
return h
test_y2 = np.linspace(-math.pi, math.pi, 100)
xx, yy = np.meshgrid(test_x2, test_y2)
test_xy = np.hstack((xx.reshape(xx.shape[0] * xx.shape[1], 1, order='F'),
yy.reshape(yy.shape[0] * yy.shape[1], 1, order='F')))
test_z2 = map(lambda t: function_f(t[0], t[1]), test_xy)
fig = plt.figure()
ax = Axes3D(fig)
num_pts = np.shape(test_xy)[0]
xs_plot = np.reshape(test_xy[:, 0], [num_pts])
ys_plot = np.reshape(test_xy[:, 1], [num_pts])
zs_plot = np.reshape(test_z2, [num_pts])
ax.plot(xs=xs_plot, ys=ys_plot, zs=xs_plot)
plt.xlabel('Samples 1')
plt.ylabel('Samples 2')
plt.zlabel('Function Value')
plt.title('Integration Function Plot')
zz = zs_plot.reshape(100, 100)
plt.figure()
plt.contour(xx, yy, zz)
plt.xlabel('Samples 1')
plt.ylabel('Samples 2')
plt.title('Integration Function Plot - contour')
print "MONTE CARLO ------------------------"
max_iterations = 50
monte_carlo_estimates = []
for i in range(0, max_iterations):
def nsga_2(input_v,activate_parallel,num_cores):
##import sys
import numpy as np
from objective_description_function import objective_description_function
from initialize_variables import initialize_variables
from non_domination_sort_mod import non_domination_sort_mod
from tournament_selection import tournament_selection
import genetic_operator
from replace_chromosome import replace_chromosome
import matplotlib.pyplot as plt
print('Running Optimization...')
input_v['objAll'] = []
input_v['xAll'] = []
import os
import timing
##Making sure thet population and generations are integers
input_v['population'] = round(input_v['population'])
input_v['generations'] = round(input_v['generations'])
##Forming the objective function
##input_v['M'] --- number of objective functions
##input_v['V'] --- number of decision variables
##input_v['min_range'] --- list of corresponding lowerbound of the decision variables
##input_v['max_range'] --- list of corresponding upperbound of the decision variables
input_v['M'], input_v['V'], input_v['min_range'], input_v['max_range'] = objective_description_function(input_v)
##Initialize the population
##Population is initialzied with random values which are within the specified range. Each chromosome consist of the
##decision variables. Also the value fo the objective functions, rank and crowding distance information are also added
##to the chromosome vector but only the elements of the vector which has the decision variables are operatedupon to
##perform the genetic operations like crossover and mutation
chromosome, input_v = initialize_variables(input_v,activate_parallel,num_cores)
##Sort the initialized population
##Sort the population using non-domination-sort. This returns two columns for each individual which are the rank and the
##crowding distance corresponding to their position in the front they belong. At this stage the rank and the crowding distance
##for each chromosome is added to the chromosome vector for easy of computation.
chromosome = non_domination_sort_mod(chromosome, input_v['M'], input_v['V'])
## Start the evolution process
# The following are performed in each generation
# * Select the parents which are fit for reproduction
# * Perfrom crossover and Mutation operator on the selected parents
# * Perform Selection from the parents and the offsprings
# * Replace the unfit individuals with the fit individuals to maintain a
# constant population size.
print('Starting search process...')
for i in range (0, input_v['generations']):
## Select the parents
## Parents are selected for reproduction to generate offspring. The
## original NSGA-II uses a binary tournament selection based on the
## crowded-comparision operator. The arguments are
## pool - size of the mating pool. It is common to have this to be half the
## population size.
## tour - Tournament size. Original NSGA-II uses a binary tournament
## selection, but to see the effect of tournament size this is kept
## arbitary, to be choosen by the user.
pool = int(round(input_v['population']/2))
tour = 2
## Selection process
## A binary tournament selection is employed in NSGA-II. In a binary
## tournament selection process two individuals are selected at random
## and their fitness is compared. The individual with better fitness is
## selcted as a parent. Tournament selection is carried out until the
## pool size is filled. Basically a pool size is the number of parents
## to be selected. The input arguments to the function
## tournament_selection are chromosome, pool, tour. The function uses
## only the information from last two elements in the chromosome vector.
## The last element has the crowding distance information while the
## penultimate element has the rank information. Selection is based on
## rank and if individuals with same rank are encountered, crowding
## distance is compared. A lower rank and higher crowding distance is
## the selection criteria.
parent_chromosome = tournament_selection(chromosome, pool, tour)
## Perfrom crossover and Mutation operator
## The original NSGA-II algorithm uses Simulated Binary Crossover (SBX) and
## Polynomial mutation. Crossover probability pc = 0.9 and mutation
## probability is pm = 1/n, where n is the number of decision variables.
## Both real-coded GA and binary-coded GA are implemented in the original
## algorithm, while in this program only the real-coded GA is considered.
## The distribution indeices for crossover and mutation operators as mu = 20
## and mum = 20 respectively.
mu = 20
mum = 20
offspring_chromosome, input_v = genetic_operator.genetic_operator(parent_chromosome, input_v['M'], input_v['V'], mu, mum,
input_v['min_range'], input_v['max_range'], input_v,num_cores,activate_parallel)
##Intermediate population
##Intermediate population is the combined population of parents and offsprings of the current generation. The population
##size is two times the initial population.
dim_chromosome = chromosome.shape
main_pop = dim_chromosome[0]
dim_offspring_chromosome = offspring_chromosome.shape
offspring_pop = dim_offspring_chromosome[0]
##Intermediate_chromosome is a concatenation of current population and the offspring population.
intermediate_chromosome = np.zeros((main_pop + offspring_pop, input_v['M'] + input_v['V']))
for j in range (0, main_pop):
for k in range (0, input_v['M'] + input_v['V']):
intermediate_chromosome[j,k] = chromosome[j,k]
for j in range (main_pop, main_pop + offspring_pop):
for k in range (0, input_v['M'] + input_v['V']):
intermediate_chromosome[j,k] = offspring_chromosome[j-main_pop,k]
##Non-domination-sort of intermediate population
##The intermediate population is sorted again based on non-domination sort before the replacement operator is performed
##on the intermediate population.
intermediate_chromosome = non_domination_sort_mod(intermediate_chromosome, input_v['M'], input_v['V'])
##np.savetxt('C:\\Optimization_zlc\\solution.csv', intermediate_chromosome, delimiter=',')
##sys.exit()
##Perform Selection
##Once the intermediate population is sorted only the best solution is selected based on it rank and crowding distance.
##Each front is filled in ascending order until the addition of population size is reached. The last front is included
##in the population based on the individuals with least crowding distance
chromosome = replace_chromosome(intermediate_chromosome, input_v['M'], input_v['V'], input_v['population'])
print('Search ' + 'iteration ' + str(i))
# if not(i%100) and (i !=0):
# disp_msg = str(i) + ' generations completed \n'
# print(disp_msg)
##Result
##Save the result in csv format
xAll = input_v['xAll']
objAll = input_v['objAll']
np.savetxt(os.path.dirname(__file__)+'\\solution\\solution.csv', chromosome, delimiter=',')
np.savetxt(os.path.dirname(__file__)+'\\solution\\solutionX.csv', xAll, delimiter=',')
np.savetxt(os.path.dirname(__file__)+'\\solution\\solutionObj.csv', objAll, delimiter=',')
##Visualize
##The following is used to visualize the result if objective space dimension can be displayed i.e. 2D, 3D
timing.log("NOW")
if input_v['M'] == 2:
##Objective functions
plt.plot(chromosome[:, input_v['V']], chromosome[:, input_v['V']+1], '*')
plt.title('Objective function last population')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.show()
##All populations
plt.plot(objAll[:, 0], objAll[:, 1], '*')
plt.title('Objective function all populations')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.show()
elif input_v['M'] == 3:
##Objective functions
plt.plot(chromosome[:, input_v['V']], chromosome[:, input_v['V']+1], chromosome[:, input_v['V']+2], '*')
plt.title('Objective function last population')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.zlabel('Objective 3')
plt.show()
##All populations
plt.plot(objAll[:,0], objAll[:,1], objAll[:,2], '*')
plt.title('Objective function all populations')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.zlabel('Objective 3')
plt.show()
##Copyright (c) 2009, Aravind Seshadri
##All rights reserved.
##Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following
##conditions are met:
## * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
## * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer
## in the documentation and/or other materials provided with the distribution
##
##THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT
##NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
##THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
##(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
##HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
##ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
plt.show()
# 3D graph (Multiple Regression)
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
ax.scatter(dataset_merged['distance'],
dataset_merged['steps'],
dataset_merged['calories'],
c='blue',
marker='o',
s=10)
ax.view_init(30, 185)
plt.xlabel('Distance')
plt.ylabel('Steps')
plt.zlabel('Calories')
plt.show()
# Fitbit
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
fitbit_dataset = pd.read_csv('fitbit_activity_a.csv')
fitbit_dataset['Steps'] = fitbit_dataset["Steps"].str.replace(",",
"").astype(float)
fitbit_dataset['Date'] = fitbit_dataset.Date.astype('datetime64[ns]')
fitbit_dataset['Calories'] = fitbit_dataset["Calories"].str.replace(
",", "").astype(float)
fitbit_dataset['Distance'] = fitbit_dataset['Distance'] * 1000
x = meters[i].sensors[j].x
y = meters[i].sensors[j].y
z = meters[i].sensors[j].z
if j == 1 or j == 2 or j == 4:
lr = left
else:
lr = right
if j == 1 or j == 3:
pos = pos1
elif j == 2 or j == 5:
pos = pos2
else:
pos = pos3
leg.append(
ax.quiver(lr,
pos,
height,
np.asarray(x),
np.asarray(y),
np.asarray(z),
length=0.1,
color=corder[j]))
plt.xlabel("<--West East--> (m)")
plt.ylabel("<--South North--> (m)")
plt.zlabel('Height (m)')
plt.title(input("Title?\n"))
for i in range(num_sensors):
plt.quiverkey(leg[i], 0.5, 0.9 - (i / 15), 25, "Sensor {}".format(i + 1))
plt.show()
def plot(self, key):
assert len((self & key)) == 1
hist = (self & key).fetch1('data')
edges = (self & key).fetch1('bin_edges')
if len(hist.shape) == 1:
plt.figure()
plt.plot(edges['0'][0][:-1], hist)
sns.despine()
plt.xlabel(key['morph_dist_name'])
plt.ylabel('P(x)')
elif len(hist.shape) == 2:
plt.figure()
if key['morph_dist_name'] == 'path angle vs branch order':
xlabel = 'path angle [deg]'
ylabel = 'branch order'
elif key['morph_dist_name'] == 'path angle vs path dist':
xlabel = 'path angle [deg]'
ylabel = 'dist from soma [microns]'
elif key['morph_dist_name'] == 'thickness vs path dist':
xlabel = 'thickness [microns]'
ylabel = 'dist from soma [microns]'
elif key['morph_dist_name'] == 'thickness vs branch order':
xlabel = 'thickness [microns]'
ylabel = 'branch order'
elif key['morph_dist_name'] == 'branch angle vs path dist':
xlabel = 'branch angle [deg]'
ylabel = 'dist from soma [microns]'
elif key['morph_dist_name'] == 'branch angle vs branch order':
xlabel = 'branch angle [deg]'
ylabel = 'branch order'
elif key[
'morph_dist_name'] == 'segment path length vs branch order':
xlabel = 'segment path length'
ylabel = 'branch order'
plt.imshow(hist.T,
extent=[0, edges['0'][0][-1], 0, edges['1'][0][-1]])
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.colorbar()
elif len(hist.shape) == 3:
x, y, z = np.meshgrid(edges['0'][0],
edges['1'][0],
edges['2'][0],
indexing='ij')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter3D(x, y, z, s=hist.flatten())
plt.xlabel('x')
plt.ylabel('y')
plt.zlabel('z')
def GeneratePlotResults(full_dataset, methods, output_dir, plots, results,
methods_d, fnames_i, title):
plot_files = []
for i in range(len(plots)):
handles, labels = [], []
fig = plt.figure(i + 1)
plt.clf()
plot = plots[i]
if 'xlim' in plot: plt.xlim(plot['xlim'][0], plot['xlim'][1])
if 'ylim' in plot: plt.ylim(plot['ylim'][0], plot['ylim'][1])
for a in plots[i]['methods']:
m = a['name']
if not m in methods_d:
continue
print str(i) + ' ' + str(m)
line_style = a['line_style'] if 'line_style' in a else methods_d[
m]['line_style']
color = a['color'] if 'color' in a else methods_d[m]['color']
if plot['type'] == 'skill':
plot = copy.deepcopy(plots[i])
plot['type'] = 'scatter' if len(
full_dataset.skill_names) <= 2 else 'scatter3d'
if not 'xlabel' in plot:
plot['xlabel'] = full_dataset.skill_names[0]
if not 'ylabel' in plot:
plot['ylabel'] = full_dataset.skill_names[1]
if not 'zlabel' in plot and len(full_dataset.skill_names) > 2:
plot['zlabel'] = full_dataset.skill_names[2]
if not 'x' in a: a['x'] = 'skill0'
if not 'y' in a: a['y'] = 'skill1'
if not 'z' in a and len(full_dataset.skill_names) > 2:
a['z'] = 'skill2'
if plot['type'] == 'semilog':
print str(len(results[m][a['x']])) + ' ' + str(
len(results[m][a['y']]))
h = plt.semilogy(results[m][a['x']],
results[m][a['y']],
line_style,
lw=plot["line-width"],
color=color)
handles.append(h[0])
elif plot['type'] == 'loglog':
h = plt.loglog(results[m][a['x']],
results[m][a['y']],
line_style,
lw=plot["line-width"],
color=color)
handles.append(h[0])
elif plot['type'] == 'plot':
h = plt.plot(results[m][a['x']],
results[m][a['y']],
line_style,
lw=plot["line-width"])
handles.append(h[0])
elif plot['type'] == 'hist':
if 'y' in a:
h = plt.hist(results[m][a['x']],
results[m][a['y']],
histtype='bar',
rwidth=plot["bar-width"],
color=plot["bar-color"])
else:
h = plt.hist(results[m][a['x']],
histtype='bar',
rwidth=plot["bar-width"],
color=plot["bar-color"])
handles.append(h[0])
elif plot['type'] == 'scatter':
h = plt.scatter(results[m][a['x']],
results[m][a['y']],
c='r',
marker='o')
handles.append(h)
elif plot['type'] == 'scatter3d':
ax = fig.add_subplot(111, projection='3d')
h = ax.scatter(results[m][a['x']],
results[m][a['y']],
results[m][a['z']],
c='r',
marker='o')
handles.append(h)
labels.append(a['title'] if 'title' in a else m)
if plot['type'] == 'scatter3d':
if 'xlabel' in plot:
ax.set_xlabel(plot['xlabel'], fontsize=plot['axis-font-size'])
if 'ylabel' in plot:
ax.set_ylabel(plot['ylabel'], fontsize=plot['axis-font-size'])
if 'zlabel' in plot:
ax.set_zlabel(plot['zlabel'], fontsize=plot['axis-font-size'])
else:
if 'xlabel' in plot:
plt.xlabel(plot['xlabel'], fontsize=plot['axis-font-size'])
if 'ylabel' in plot:
plt.ylabel(plot['ylabel'], fontsize=plot['axis-font-size'])
if 'zlabel' in plot:
plt.zlabel(plot['zlabel'], fontsize=plot['axis-font-size'])
if 'title' in plot:
plt.title(plot['title'], fontsize=plot['title-font-size'])
plt.tick_params(axis='both',
which='major',
labelsize=plot['tick-font-size'])
plt.tick_params(axis='both',
which='minor',
labelsize=plot['tick-font-size'])
if 'legend' in plot:
plt.legend(handles,
labels,
prop={'size': plot['legend-font-size']})
plt.savefig(os.path.join('output', output_dir, plot['name'] + '.pdf'))
plt.savefig(os.path.join('output', output_dir, plot['name'] + '.png'))
plot_files.append(output_dir + '/' + plot['name'] + '.png')
with open(os.path.join('output', output_dir, 'galleries.json'), 'w') as f:
json.dump({
'plots': plot_files,
'methods': fnames_i,
'title': title
}, f)
def plot_results(mode,sp,x,xT,args):
if not args or not args[0]:
b_obs = False
else:
b_obs = True
obs = args[0]
d, _, nbSPoint = x.shape
if mode=='i':
if d==2:
sp['fig'] = plt.figure(num=0) #,'2D Simulation of the task',figsize=(653 550 560 420)
plt.title(r'2D Simulation of the task')
sp['axis'] = plt.gca()
hold(True)
sp['xT'] = plt.plot(xT[0],xT[1],'k*', markersize=10, linewidth=1.5)
sp['xT_l'] = plt.plot(xT[0],xT[1],'k--', linewidth=1.5)
for j in range(nbSPoint):
plot(x[0,0,j],x[1,0,j],'ok',markersize=2,linewidth=7.5)
sp['x['str(j)']']= plot(x[0,0,j],x[1,0,j])
plt.xlabel(r'$\xi_1$',interpreter=latex,fontsize=16)
plt.ylabel(r'$\xi_2$',interpreter=latex,fontsize=16)
plt.show()
if b_obs:
x_obs, x_obs_sf = obs_draw_ellipsoid(obs,40)
for n in range(x_obs.shape[3]):
sp['obs'][n] = patch(x_obs[0,:,n],x_obs[1,:,n],0.1*np.ones((1,x_obs.shape[2])),[0.6 1 0.6])
sp['obs_sf'][n] = plot(x_obs_sf[0,:,n],x_obs_sf[1,:,n],'k--',linewidth=0.5)
elif d==3:
sp['fig'] = plt.figure(num=0)
plt.title('3D Simulation of the task')
sp['axis'] = plt.gca()
ax = sp['fig'].add_subplot(111, projection='3d')
sp['xT'] = ax.plot(xT[0],xT[1],xT[2],'k*',markersize=10,linewidth=1.5)
sp['xT_l'] = ax.plot(xT[0],xT[1],xT[2],'k--',linewidth=1.5)
for j in range(0, nbSPoint-1):
ax.plot(x[0,0,j],x[1,0,j],x[2,0,j],markersize=2,linewidth=7.5)
sp['x'][j]= ax.plot(x[0,0,j],x[1,0,j],x[2,0,j])
if b_obs:
n_theta = 15
n_phi = 10
x_obs = obs_draw_ellipsoid(obs,[n_theta, n_phi])
for n in range(0, np.size(x_obs,2) - 1):
sp['obs'][n] = ax.plot_surface(np.reshape(x_obs[0,:,n],n_phi,n_theta), np.reshape(x_obs[1,:,n],n_phi,n_theta), np.reshape(x_obs[2,:,n],n_phi,n_theta))
plt.setp(sp['obs'][n],color=[0.6, 1, 0.6],linewidth=0.1)
plt.xlabel(r'$\xi_1$',interpreter=latex,fontsize=16)
plt.ylabel(r'$\xi_2$',interpreter=latex,fontsize=16)
plt.zlabel(r'$\xi_3$',interpreter=latex,fontsize=16)
plt.show()
else:
sp['fig'] = plt.figure(num=0)
plt.title('Simulation of the task')
for i in range(1, d-1):
sp.['axis'][i-1]=sp['fig'].add_subplot(d-1,0,i-1)
sp['xT'][i-1] = plt.plot(xT[0],xT[i],'k*',markersize=10,linewidth=1.5)
sp.['xT_l'][i-1] = plt.plot(xT[0],xT[i],'k--',linewidth=1.5)
for j in range(0, nbSPoint - 1):
plt.plot(x[0,0,j],x[i,0,j],markersize=2,linewidth=7.5);
sp['x'][i-1,j]= plt.plot(x[0,0,j],x[i,0,j])
plt.ylabel(r'$\xi_' + str(i) + '$',interpreter=latex,fontsize=12)
if i==d :
plt.xlabel(r'$\xi_1$',interpreter=latex,fontsize=12);
plt.show()
if mode=='u': #updating the figure
if plt.gcf() != sp['fig']:
plt.figure(sp['fig'])
if d==2:
ax=sp['axis'];
for j in range(0, nbSPoint - 1):
sp['x'][j].set_xdata(x[1,:,j])
sp['x'][j].set_ydata(x[1,:,j])
if np.maximum(x[0,end,:])>ax.get_xlim(1) or np.minimum(x[0,end,:])<ax.get_xlim(0)
or np.maximmum(x[1,end,:])>ax.get_ylim(1) or np.minimum(x[1,end,:])<ax.get_ylim(0):
plt.autoscale(enable=True, axis=sp['axis'], tight=True)
ax=sp['axis'];
sp['fig'].add_subplot(ax,
[ax.get_xlim(0)-(ax.get_xlim(1)-ax.get_xlim(0))/10, ax.get_xlim(1)+(ax.get_xlim(1)-ax.get_xlim(0))/10,
ax.get_ylim(0)-(ax.get_ylim(1)-ax.get_ylim(0))/10 ax.get_ylim(1)+(ax.get_ylim(1)-ax.get_ylim(0))/10])
elif d==3:
ax=sp['axis'];
for j in range(0, nbSPoint - 1):
sp.['x'][j].set_xdata(x[0,:,j])
sp.['x'][j].set_ydata(x[1,:,j])
sp.['x'][j].set_zdata(x[2,:,j])
if np.maximum(x[0,end,:])>ax.get_xlim(1) or np.minimum(x[0,end,:])<ax.get_xlim(0)
or np.maximum(x[1,end,:])>ax.get_ylim(1) or np.minimum(x[1,end,:])<ax.get_ylim(1) or
np.maximum(x[2,end,:])>ax.get_zlim(1) or np.minimum(x[2,end,:])<ax.get_zlim(0):
plt.autoscale(enable=True, axis=sp['axis'], tight=True)
ax=sp['axis']
sp['fig'].add_subplot(ax,
get_xlim(0)-(ax.get_xlim(1)-ax.get_xlim(0))/10, ax.get_xlim(1)+(ax.get_xlim(1)-ax.get_xlim(0))/10,
ax.get_ylim(0)-(ax.get_ylim(1)-ax.get_ylim(0))/10 ax.get_ylim(1)+(ax.get_ylim(1)-ax.get_ylim(0))/10,
ax.get_zlim(0)-(ax.get_zlim(1)-ax.get_zlim(0))/10 ax.get_zlim(1)+(ax.get_zlim(1)-ax.get_zlim(0))/10])
## plt.show()
## savefig('Pressure_at_left.png')
# ---- VELOCITY ---- #
y = 0.0
fig = plt.figure()
ax = fig.gca(projection='3d')
for j in range(40,78)
u = []
for k in range(1,nRows):
u.append(a[k][j])
ax.plot(time, u, y)
y = y+0.021875
plt.xlabel('time [sec]')
plt.ylabel('velocity [m/s]')
plt.zlabel('height [m]')
plt.suptitle('Velocity')
plt.grid(True)
plt.show()
savefig('Velocities_at_left.png')
#####################################################################################
### Print an output file to validate the results
## maxPressureCal = max(pressure2)
## maxPressureRef = 999.53
## err = 100*abs(maxPressureRef-maxPressureCal)/maxPressureRef
## val = open('validation_pressure.txt', 'w')
## val.write('Only for gauges taken at (x,y)=(0.5,0.5)'+'\n')
## val.write('Maximum pressure:'+'\n')
## val.write('Reference'+'\t'+'Simulation'+'\t'+'\t'+'Error'+'\n')
import matplotlib.pyplot as plt
import pyexcel as p
graph = p.get_excel('/home/cl311/Rathi/xyzgraph.csv')
plt.plot(x, y, z)
plt.xlabel("X AXIS")
plt.ylabel("Y AXIS")
plt.zlabel("Z AXIS")
plt.title("PLOTTING A GRAPH FROM EXCEL")
plt.show()
from sklearn.preprocessing import MinMaxScaler
import warnings
warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
for i in range(1,2):
filename = "./data_skin/skin_benchmark_"+'{0:04}'.format(i)+".csv"
df = pd.read_csv(filename)
print(df.describe())
show = False
if show:
plt.figure(figsize=(10,10))
plt.scatter(df['R'],df['G'],df['B'])
plt.xlabel("R")
plt.ylabel("G")
plt.zlabel("B")
plt.show()
scaler = MinMaxScaler(feature_range=(0,1))
df[['R','G','B']] = scaler.fit_transform(df[['R','G','B']])
print(df[['R','G','B']].head())
t1=df['ground.truth'].value_counts(normalize=True)
t2=df['ground.truth'].value_counts(normalize=False)
x1 = df['R'].values.reshape(-1,1)
x2 = df['G'].values.reshape(-1,1)
x3 = df['B'].values.reshape(-1,1)
x = np.concatenate((x1,x2,x3),axis=1)
# 设置离群点数据
random_state = np.random.RandomState(42)
outliers_fraction = t1["anomaly"]
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 18 16:40:48 2018
@author: mmvillangca
tri-surface from point cloud
"""
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
coor = np.loadtxt('insertPointCloudFileHere.txt')
x = coor[:, 0]
y = coor[:, 1]
z = coor[:, 2]
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_aspect('equal')
ax.plot_trisurf(x, y, z, linewidth=0.2, antialiased=True, shade=True)
plt.xlabel('x', fontsize=14)
plt.ylabel('y', fontsize=14)
plt.zlabel('y', fontsize=14)
plt.show()
def plot_trajectory(self):
font1 = {
'family': 'Times New Roman',
'weight': 'normal',
'size': 20,
}
font2 = {
'family': 'Times New Roman',
'weight': 'normal',
'size': 18,
}
fig = plt.figure(figsize=(10, 10))
if self.plot_3d:
ax1 = fig.add_subplot(111, projection='3d')
else:
ax1 = fig.add_subplot(111)
Gt_traj = self.GtPose[:, :3, 3]
if self.rotate:
rotate_theta = -np.pi / 4
Gt_traj = np.dot(
Gt_traj,
np.array([[np.cos(rotate_theta), -np.sin(rotate_theta)],
[np.sin(rotate_theta),
np.cos(rotate_theta)]]))
if self.plot_3d:
ax1.plot(Gt_traj[:, 0],
Gt_traj[:, 1],
Gt_traj[:, 2],
'--',
linewidth=4,
color='k')
else:
ax1.plot(Gt_traj[:, 0],
Gt_traj[:, 2],
'--',
linewidth=4,
color='k')
Lo_traj = self.LoPose[:, :3, 3]
if self.rotate:
rotate_theta = np.pi
Lo_traj = np.dot(
Lo_traj,
np.array([[np.cos(rotate_theta), -np.sin(rotate_theta)],
[np.sin(rotate_theta),
np.cos(rotate_theta)]]))
if self.plot_3d:
ax1.plot(Lo_traj[:, 0],
Lo_traj[:, 1],
Lo_traj[:, 2],
linewidth=4,
color='r')
else:
ax1.plot(Lo_traj[:, 0], Lo_traj[:, 2], linewidth=4, color='r')
if self.plot_loop_closure:
Ls_traj = self.LsPose[:, :3, 3]
if self.rotate:
rotate_theta = np.pi
Ls_traj = np.dot(
Ls_traj,
np.array([[np.cos(rotate_theta), -np.sin(rotate_theta)],
[np.sin(rotate_theta),
np.cos(rotate_theta)]]))
if self.plot_3d:
ax1.plot(Ls_traj[:, 0],
Ls_traj[:, 1],
Ls_traj[:, 2],
linewidth=4,
color='b')
else:
ax1.plot(Ls_traj[:, 0], Ls_traj[:, 2], linewidth=4, color='b')
plt.tick_params(labelsize=14)
labels = ax1.get_xticklabels() + ax1.get_yticklabels()
[label.set_fontname('Times New Roman') for label in labels]
#plt.title('Comparison of trajectory')
if self.plot_3d:
plt.xlabel('x(m)', font2)
plt.ylabel('y(m)', font2)
plt.zlabel('z(m)', font2)
else:
plt.xlabel('x(m)', font2)
plt.ylabel('z(m)', font2)
plt.axis("equal")
if self.plot_loop_closure:
plt.legend(('Ground truth', 'Lidar odometry', 'Lidar SLAM'),
prop=font1)
else:
plt.legend(('Ground truth', 'Lidar odometry'), prop=font1)
plt.savefig(self.saving_path + "_trajectory.jpg", dpi=500)
plt.show()
clusterNum = 3
k_means = KMeans(init="k-means++", n_clusters=clusterNum, n_init=12)
k_means.fit(X)
labels = k_means.labels_
df["Clus_km"] = labels
df.head(5)
df.groupby('Clus_km').mean()
area = np.pi * (X[:, 1])**2
plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5)
plt.xlabel('Age', fontsize=18)
plt.ylabel('Income', fontsize=16)
plt.show()
fig = plt.figure(1, figsize=(8, 6))
plt.clf()
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
plt.cla()
plt.ylabel('Age', fontsize=18)
plt.xlabel('Income', fontsize=16)
plt.zlabel('Education', fontsize=16)
ax.set_xlabel('Education')
ax.set_ylabel('Age')
ax.set_zlabel('Income')
ax.scatter(X[:, 1], X[:, 0], X[:, 3], c=labels.astype(np.float))
# calculate the function value: z = x^2 + sin(y^2)
Z = (X**2) + np.sin(Y**2)
# initialize the 3D figure
fig = plt.figure(figsize=(10, 6))
ax = fig.gca(projection='3d')
# plot the surface
ax.plot_surface(X, Y, Z, cmap='Reds')
# add labels
plt.xlabel('x')
plt.ylabel('y')
plt.title('f(x, y)')
plt.zlabel('z')
plt.show()
# Let's take a step further and plot the 3D surface side-by-side with the slices. Run this code **as-is** and play around with the interactive sliders.
#
# **NOTE:** The code may be slow to refresh, so be patient.
# In[74]:
def plot_surface_problem3(x0, y0):
# resolution
delta = 0.1
def nsga_2(input_v, activate_parallel, num_cores, crossover_perc,
mutation_perc, obj_func_plot):
##import sys
import numpy as np
from objective_description_function import objective_description_function
from initialize_variables import initialize_variables
from non_domination_sort_mod import non_domination_sort_mod
from tournament_selection import tournament_selection
import genetic_operator_adv
from replace_chromosome import replace_chromosome
import matplotlib.pyplot as plt
from ga_mono_binary_conv_info import ga_mono_binary_conv_info
print('Running Optimization...')
input_v['objAll'] = []
input_v['xAll'] = []
import os
import timing
import time
##Making sure thet population and generations are integers
input_v['population'] = round(input_v['population'])
input_v['generations'] = round(input_v['generations'])
bin_info_variable_list = ga_mono_binary_conv_info(
input_v['range_of_decision_variables'])
total_binary_len = sum(bin_info_variable_list['Bits'][:])
##Forming the objective function
##input_v['M'] --- number of objective functions
##input_v['V'] --- number of decision variables
##input_v['min_range'] --- list of corresponding lowerbound of the decision variables
##input_v['max_range'] --- list of corresponding upperbound of the decision variables
input_v['M'], input_v['V'], input_v['min_range'], input_v[
'max_range'] = objective_description_function(input_v)
##Initialize the population
##Population is initialzied with random values which are within the specified range. Each chromosome consist of the
##decision variables. Also the value fo the objective functions, rank and crowding distance information are also added
##to the chromosome vector but only the elements of the vector which has the decision variables are operatedupon to
##perform the genetic operations like crossover and mutation
chromosome, input_v = initialize_variables(input_v, activate_parallel,
num_cores,
bin_info_variable_list)
##Sort the initialized population
##Sort the population using non-domination-sort. This returns two columns for each individual which are the rank and the
##crowding distance corresponding to their position in the front they belong. At this stage the rank and the crowding distance
##for each chromosome is added to the chromosome vector for easy of computation.
chromosome = non_domination_sort_mod(chromosome, input_v['M'],
input_v['V'])
## Start the evolution process
# The following are performed in each generation
# * Select the parents which are fit for reproduction
# * Perfrom crossover and Mutation operator on the selected parents
# * Perform Selection from the parents and the offsprings
# * Replace the unfit individuals with the fit individuals to maintain a
# constant population size.
##Generating probability reference for the mutation process
mutation_prob = mutation_probab(bin_info_variable_list, total_binary_len)
axes = plt.gca()
print('Starting search process...')
for i in range(0, input_v['generations']):
## Select the parents
## Parents are selected for reproduction to generate offspring. The
## original NSGA-II uses a binary tournament selection based on the
## crowded-comparision operator. The arguments are
## pool - size of the mating pool. It is common to have this to be half the
## population size.
## tour - Tournament size. Original NSGA-II uses a binary tournament
## selection, but to see the effect of tournament size this is kept
## arbitary, to be choosen by the user.
pool = int(round(input_v['population'] / 2))
tour = 2
## Selection process
## A binary tournament selection is employed in NSGA-II. In a binary
## tournament selection process two individuals are selected at random
## and their fitness is compared. The individual with better fitness is
## selcted as a parent. Tournament selection is carried out until the
## pool size is filled. Basically a pool size is the number of parents
## to be selected. The input arguments to the function
## tournament_selection are chromosome, pool, tour. The function uses
## only the information from last two elements in the chromosome vector.
## The last element has the crowding distance information while the
## penultimate element has the rank information. Selection is based on
## rank and if individuals with same rank are encountered, crowding
## distance is compared. A lower rank and higher crowding distance is
## the selection criteria.
parent_chromosome = tournament_selection(chromosome, pool, tour)
## Perfrom crossover and Mutation operator
## The original NSGA-II algorithm uses Simulated Binary Crossover (SBX) and
## Polynomial mutation. Crossover probability pc = 0.9 and mutation
## probability is pm = 1/n, where n is the number of decision variables.
## Both real-coded GA and binary-coded GA are implemented in the original
## algorithm, while in this program only the real-coded GA is considered.
## The distribution indeices for crossover and mutation operators as mu = 20
## and mum = 20 respectively.
mu = 20
mum = 20
parent_chrom_bin = conv_dec_to_bin(bin_info_variable_list,
parent_chromosome, input_v['M'])
offspring_chromosome, input_v = genetic_operator_adv.genetic_operator_adv(
parent_chrom_bin, mutation_prob, input_v, bin_info_variable_list,
activate_parallel, num_cores)
##Intermediate population
##Intermediate population is the combined population of parents and offsprings of the current generation. The population
##size is two times the initial population.
dim_chromosome = chromosome.shape
main_pop = dim_chromosome[0]
dim_offspring_chromosome = offspring_chromosome.shape
offspring_pop = dim_offspring_chromosome[0]
##Intermediate_chromosome is a concatenation of current population and the offspring population.
intermediate_chromosome = np.zeros(
(main_pop + offspring_pop, input_v['M'] + input_v['V']))
for j in range(0, main_pop):
for k in range(0, input_v['M'] + input_v['V']):
intermediate_chromosome[j, k] = chromosome[j, k]
for j in range(main_pop, main_pop + offspring_pop):
for k in range(0, input_v['M'] + input_v['V']):
intermediate_chromosome[j,
k] = offspring_chromosome[j - main_pop,
k]
##Non-domination-sort of intermediate population
##The intermediate population is sorted again based on non-domination sort before the replacement operator is performed
##on the intermediate population.
intermediate_chromosome = non_domination_sort_mod(
intermediate_chromosome, input_v['M'], input_v['V'])
##np.savetxt('C:\\Optimization_zlc\\solution.csv', intermediate_chromosome, delimiter=',')
##sys.exit()
##Perform Selection
##Once the intermediate population is sorted only the best solution is selected based on it rank and crowding distance.
##Each front is filled in ascending order until the addition of population size is reached. The last front is included
##in the population based on the individuals with least crowding distance
chromosome = replace_chromosome(intermediate_chromosome, input_v['M'],
input_v['V'], input_v['population'])
if obj_func_plot == 'yes':
plt.plot(chromosome[:, input_v['V']],
chromosome[:, input_v['V'] + 1], '*')
plt.title('Objective function last population')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.draw()
plt.pause(1e-17)
axes.clear()
print('Search ' + 'iteration ' + str(i))
# if not(i%100) and (i !=0):
# disp_msg = str(i) + ' generations completed \n'
# print(disp_msg)
##Result
##Save the result in csv format
xAll = input_v['xAll']
objAll = input_v['objAll']
np.savetxt(os.path.dirname(__file__) + '\\solution\\solution.csv',
chromosome,
delimiter=',')
np.savetxt(os.path.dirname(__file__) + '\\solution\\solutionX.csv',
xAll,
delimiter=',')
np.savetxt(os.path.dirname(__file__) + '\\solution\\solutionObj.csv',
objAll,
delimiter=',')
##Visualize
##The following is used to visualize the result if objective space dimension can be displayed i.e. 2D, 3D
timing.log("NOW")
if input_v['M'] == 2:
##Objective functions
plt.plot(chromosome[:, input_v['V']], chromosome[:, input_v['V'] + 1],
'*')
plt.title('Objective function last population')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.show()
##All populations
plt.plot(objAll[:, 0], objAll[:, 1], '*')
plt.title('Objective function all populations')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.show()
elif input_v['M'] == 3:
##Objective functions
plt.plot(chromosome[:, input_v['V']], chromosome[:, input_v['V'] + 1],
chromosome[:, input_v['V'] + 2], '*')
plt.title('Objective function last population')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.zlabel('Objective 3')
plt.show()
##All populations
plt.plot(objAll[:, 0], objAll[:, 1], objAll[:, 2], '*')
plt.title('Objective function all populations')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.zlabel('Objective 3')
plt.show()
# numbers of limes of different species sold each month
key_limes_per_month = [92.0, 109.0, 124.0, 70.0, 101.0, 79.0, 106.0, 101.0, 103.0, 90.0, 102.0, 106.0]
persian_limes_per_month = [67.0, 51.0, 57.0, 54.0, 83.0, 90.0, 52.0, 63.0, 51.0, 44.0, 64.0, 78.0]
blood_limes_per_month = [75.0, 75.0, 76.0, 71.0, 74.0, 77.0, 69.0, 80.0, 63.0, 69.0, 73.0, 82.0]
plt.figure(figsize=12,8)
ax1=plt.subplot(1,2,1)
x_values=range(len(12))
plt.plot(x_values,visits_per_month,marker='*')
plt.title('Amount of visits per months')
plt.xlabel('Months')
plt.ylabel('Visits')
ax.set_xticks(x_values)
ax.set_xticklabels(months)
#ax.set_yticks([0.10, 0.25, 0.5, 0.75])
#ax.set_yticklabels(["10%", "25%", "50%", "75%"])
ax2=plt.subplot(1,2,2)
plt.plot(persian_limes_per_month,months,marker='s')
plt.plot([x_values,
key_limes_per_month,color='orange'],[x_values,persian_limes_per_month,color='blue'],[x_values,blood_limes_per_month,color='purple'])#nose si es asi
plt.xlabel('key limes')
plt.ylabel('persian limes')
plt.zlabel('blood limes')
ax.set_xticks(x_values)
ax.set_xticklabels(months)
plt.title('LIMES')
plt.savefig('plot.png')
plt.show()
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
fig = plt.figure()
fig.clf()
ax = fig.gca(projection='3d')
plt.xlabel('MAX')
plt.ylabel('NUM')
plt.zlabel('AVG')
ax.scatter(x, y, z)
ax.set_zlim(0.2, 0.5)
ax.set_xlabel('Max features')
ax.set_ylabel('Num estimator')
ax.set_zlabel('Avg accuracy')
plt.show()
x_coord = np.empty(core.size)
y_coord = np.empty(core.size)
z_coord = np.empty(core.size)
p_size = np.empty(core.size)
for i in range(core.shape[0]):
for j in range(core.shape[1]):
for k in range(core.shape[2]):
x_coord[i*core.shape[1]*core.shape[2] + j*core.shape[2] + k] = i
y_coord[i*core.shape[1]*core.shape[2] + j*core.shape[2] + k] = j
z_coord[i*core.shape[1]*core.shape[2] + j*core.shape[2] + k] = k
p_size[i*core.shape[1]*core.shape[2] + j*core.shape[2] + k] = core[i,j,k]* 5
fig = plt.figure()
ax = Axes3D(fig)
plt.xlabel('neurons')
plt.ylabel('stimulus category')
plt.zlabel('time slot')
ax.scatter(x_coord, y_coord, z_coord, s=np.abs(p_size))
plt.show()
time_good = np.zeros(8)
for i in range(8):
time_good[i] = np.sum(core[:,:,:(i+1)]) / np.sum(core)
plt.title('Representation power on Time')
plt.xlabel('# of PCs')
plt.ylabel('Accum % of total variance')
plt.plot(time_good)
plt.show()
stimulus_good = np.zeros(8)
for i in range(8):