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)