def draw(xy_matrix, info='<None>'):
"""Draw an XY matrix and attach some info"""
from matplotlib import pyplot
pyplot.copper()
pyplot.matshow(xy_matrix)
pyplot.xlabel(info, color="red")
pyplot.show()
def not_used04():
values = np.random.rand(10, 10)
print(values)
print(values.shape)
plt.imshow(values) # image show
plt.copper() # colormap 변경, default viridis
plt.show()
def main():
global switchBool
global x
z = 0
a = 0
c = 0
d = 0
e = 0
plt.axis([0, 100, 0, 50])
i = 0
# for i in range(50):
while i < 1000:
i = i + 1
events = get_key()
if events:
x = int(events[0].state)
switchBool = not switchBool
if switchBool:
d = x
if x == 72:
a = 1
elif x == 80:
a = -1
elif x == 77:
a = 0
z = z + a
plt.plot(i / 10, z, 'ro')
plt.plot(c / 10, e, 'bo')
print(i / 10, z, "heyo")
print(c / 10, e, "oyeh")
c = i
e = z
print(i / 10, z, " heyo")
print(c / 10, e, " oyeh")
plt.pause(0.001)
print(i)
plt.copper()
plt.autoscale(enable=True, axis='both', tight=False)
#plt.autoscale(enable=True, axis='y', tight=False)
plt.show()
def renderGraphs(data):
font = {'size': 10, 'color': 'blue'}
if not os.path.exists('./grafer'):
os.mkdir('./grafer')
for case in data.keys():
mpl.copper()
for algorithm in data[case].keys():
mpl.plot(list(data[case][algorithm].keys()),
list(data[case][algorithm].values()))
mpl.legend(data[case].keys())
mpl.grid(True)
mpl.title('Skaleringstest - {}'.format(case))
mpl.tick_params(axis='x', labelrotation=45, labelsize=6)
mpl.xlabel('Elementer i listen')
mpl.ylabel('Tid')
mpl.savefig('./grafer/{}.png'.format(case), dpi=300)
mpl.clf()
def main():
global switchBool
global x
z =0
a = 0
c = 0
d = 0
plt.axis([0, 100, 0, 50])
i = 0
# for i in range(50):
while i < 1000:
i = i + 1
events = get_key()
if events:
x = int(events[0].state)
switchBool = not switchBool
if switchBool:
d = x
if x == 72:
a = 1
elif x == 80:
a = -1
y = m.sin(i/10)
z = d
b = (c + a)*y
c = b
print("x ",x)
print("b" ,b)
plt.scatter(i/2, b)
plt.pause(0.001)
print(i)
plt.copper()
plt.autoscale(enable=True, axis='both', tight=False)
plt.show()
def do_plot(
self,
axs,
axs_labels=None,
color='r',
marker='.',
lw=1,
label="Label"
): # FIXME what is len(axs) is > 3 ? Use Multidim Scaling or Feature selection
if axs_labels is None:
axs_labels = ["Axis " + str(i + 1) for i in range(len(axs))]
if len(axs) == 1:
axs = [range(len(axs[0]))] + axs
axs_labels = ["Samples"] + axs_labels
elif len(axs) > 3:
axs = list(zip(*self.PCA_Transform(axs)))
if self.plots is None:
self.start_plot(axs_labels)
if all([isinstance(v, datetime.date) for v in axs[0]]):
self.plots.set_xlim([min(axs[0]), max(axs[0])])
plt.gcf().autofmt_xdate()
self.plots.xaxis.set_major_locator(MinuteLocator(interval=15))
self.plots.xaxis.set_major_formatter(
DateFormatter("%Y-%m-%d %H:%M"))
if marker == '-':
self.plots.plot(*axs, c=color, lw=lw, label=label)
else:
self.plots.scatter(*axs,
c=color,
marker=marker,
lw=self.lw,
s=self.s,
cmap=plt.copper(),
label=label)
self.plots.legend(loc='best', ncol=2)
import time
overall_start = time.time()
import nengo
import build
import numpy as np
import matplotlib.pyplot as plt
plt.copper()
import argparse
from mytools import hrr, nf, fh, nengo_plot_helper, extract_probe_data
import random
from matplotlib import rc, font_manager
plt.rc('text', usetex=True)
plt.rc('axes', color_cycle=['gray'])
seed = 510
random.seed(seed)
sim_class = nengo.Simulator
learning_time = 2 #in seconds
testing_time = 0.1 #in seconds
ttms = testing_time * 1000 #in ms
hrr_num = 1
DperE = 64
dim = 64
num_ensembles = int(dim / DperE)
dim = num_ensembles * DperE
def do_plot(self, axs, axs_labels = None, color = 'r', marker = '.'): # FIXME what is len(axs) is > 3 ? Use Multidim Scaling or Feature selection if axs_labels is None: axs_labels = [ "Axis "+str(i+1) for i in range( len(axs) ) ] if len(axs) == 1: axs = [ range( len(axs[0]) ) ] + axs axs_labels = [ "Samples" ] + axs_labels if self.plots is None: self.start_plot( axs_labels ) if marker == '-': self.plots.plot( *axs, c = color, lw = 1 ) else: self.plots.scatter( *axs, c = color, marker = marker, lw = self.lw, s = self.s, cmap = plt.copper() ) # Re adjusting the xrange, yrange and zrange limits FIXME '''
def query_disagreement_test(self): ids, _ = self.query_margin() scores = [] plots_Y = []; plots_X0 = []; plots_X1 = []; plots_X2 = []; plots_X3 = []; plots_X4 = []; plots_X5 = []; plots_X6 = []; viz = Visualize() commitee = [] for idp, dp in enumerate(self.Ux): if idp in ids[:self.optimize]: true_y = self.Uy[idp] # true_y = self.clf.predict_label(dp) temp_clf = Classification(self.Lx + [dp], self.Ly + [true_y], method = self.clf.method) temp_clf.GAMMA, temp_clf.C = self.clf.GAMMA, self.clf.C; temp_clf.train() commitee.append( (temp_clf, 1) ) # =========================== # sampled = random.sample(ids, 100) for ix, x in enumerate(self.Ux): # if ix in sampled: if ix in ids[:self.optimize*9999999]: informativeness1 = self.get_disag1(x, weighted = False) informativeness2 = self.get_disag2(x, commitee, weighted = False) informativeness3 = self.get_disag1(x, weighted = True) informativeness4 = self.get_disag2(x, commitee, weighted = True) informativeness5 = self.clf.uncertainty_prediction(x) informativeness6 = self.get_balance(x) temp_clf = Classification(self.Lx + [x], self.Ly + [self.Uy[ix]], method = self.clf.method) temp_clf.GAMMA, temp_clf.C = self.clf.GAMMA, self.clf.C; temp_clf.train() acc = temp_clf.getTestAccuracy( self.Tx, self.Ty ) plots_X0.append( acc ) plots_X1.append( informativeness1 ) plots_X2.append( informativeness2 ) plots_X3.append( informativeness3 ) plots_X4.append( informativeness4 ) plots_X5.append( informativeness5 ) plots_X6.append( informativeness6 ) plots_Y.append( 'r' if self.Uy[ix] != self.clf.predict_label(x) else 'b' ) fig, axs = plt.subplots( 1, 1, sharex=True ) axs.scatter( plots_X1, plots_X2, c = plots_Y, marker = "o", cmap = plt.copper() ) plt.savefig(str(len(self.Lx)) + self.datasetname+'.1-2.png'); plt.close() fig, axs = plt.subplots( 1, 1, sharex=True ) axs.scatter( plots_X3, plots_X4, c = plots_Y, marker = "o", cmap = plt.copper() ) plt.savefig(str(len(self.Lx)) + self.datasetname+'.3-4.png'); plt.close() fig, axs = plt.subplots( 1, 1, sharex=True ) axs.scatter( plots_X1, plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) plt.savefig(str(len(self.Lx)) + self.datasetname+'.1-acc.png'); plt.close() fig, axs = plt.subplots( 1, 1, sharex=True ) axs.scatter( plots_X2, plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) plt.savefig(str(len(self.Lx)) + self.datasetname+'.2-acc.png'); plt.close() fig, axs = plt.subplots( 1, 1, sharex=True ) axs.scatter( plots_X3, plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) plt.savefig(str(len(self.Lx)) + self.datasetname+'.3-acc.png'); plt.close() fig, axs = plt.subplots( 1, 1, sharex=True ) axs.scatter( plots_X4, plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) plt.savefig(str(len(self.Lx)) + self.datasetname+'.4-acc.png'); plt.close() fig, axs = plt.subplots( 1, 1, sharex=True ) axs.scatter( plots_X5, plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) plt.savefig(str(len(self.Lx)) + self.datasetname+'.5-acc.png'); plt.close() fig, axs = plt.subplots( 1, 1, sharex=True ) axs.scatter( plots_X6, plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) plt.savefig(str(len(self.Lx)) + self.datasetname+'.6-acc.png'); plt.close() # plots = [ plots_X1, plots_X2, plots_X3, plots_X4, plots_X5, plots_X6 ] # fig, axs = plt.subplots( 5, 1, sharex=True ) # axs[0].scatter( Util.normalize(plots_X1), plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) # axs[1].scatter( Util.normalize(plots_X2), plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) # axs[2].scatter( Util.normalize(plots_X3), plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) # axs[3].scatter( Util.normalize(plots_X4), plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) # axs[4].scatter( Util.normalize(plots_X5), plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) # axs[5].scatter( Util.normalize(plots_X6), plots_X0, c = plots_Y, marker = "o", cmap = plt.copper() ) # plt.savefig(str(len(self.Lx)) + self.datasetname+'.png') # plt.close() informativeness = acc else: informativeness = 0. scores.append( informativeness ) return self.sort_scores(scores)
def do_plot(self, axs, axs_labels = None, color = 'r', marker = '.', lw = 1, label="Label"): # FIXME what is len(axs) is > 3 ? Use Multidim Scaling or Feature selection
if axs_labels is None:
axs_labels = [ "Axis "+str(i+1) for i in range( len(axs) ) ]
if len(axs) == 1:
axs = [ range( len(axs[0]) ) ] + axs
axs_labels = [ "Samples" ] + axs_labels
elif len(axs) > 3:
axs = list(zip(*self.PCA_Transform(axs)))
if self.plots is None:
self.start_plot( axs_labels )
if all([ isinstance(v, datetime.date) for v in axs[0] ]):
self.plots.set_xlim([ min(axs[0]), max(axs[0]) ])
plt.gcf().autofmt_xdate()
self.plots.xaxis.set_major_locator(MinuteLocator(interval=15))
self.plots.xaxis.set_major_formatter( DateFormatter("%Y-%m-%d %H:%M") )
if marker == '-':
self.plots.plot( *axs, c = color, lw = lw, label=label )
else:
self.plots.scatter( *axs, c = color, marker = marker, lw = self.lw, s = self.s, cmap = plt.copper(), label=label )
self.plots.legend(loc='best', ncol=2)