def getGraph(): for i, clf in enumerate((svm, rbf_svc, rbf_svc_tunning)): # Se grafican las fronteras plt.subplot(2, 2, i + 1) plt.subplots_adjust(wspace=0.4, hspace=0.4) Z = clf.predict(np.c_[x_matrizSetEntrenamientoVect, y_clases]) #Color en las gráficas Z = Z.reshape(x_matrizSetEntrenamientoVect.shape) plt.contourf(x_matrizSetEntrenamientoVect, y_clases, Z, cmap=plt.cm.Paired, alpha=0.8) #Puntos de entrenamiento plt.scatter(x_matrizSetEntrenamientoVect[:, 0], x_matrizSetEntrenamientoVect[:, 1], c=y_clases, cmap=plt.cm.Paired) plt.xlabel('Longitud Sepal') plt.ylabel('Peso Sepal') plt.xlim(x_matrizSetEntrenamientoVect.min(), x_matrizSetEntrenamientoVect.max()) plt.ylim(y_clases.min(), y_clases.max()) plt.xticks(()) plt.yticks(()) plt.title(titles[i]) plt.show()
def tmp(cm, acts): import matplotlib.pyplot as plt import numpy as np plt.imshow(cm, interpolation='nearest') plt.xticks(np.arange(0, len(acts)), acts) plt.yticks(np.arange(0, len(acts)), acts)
def plot(): plt.figure(figsize=(20, 10)) width = 0.5 index = np.arange(26) print 'SUM PLOT 1', sum(row[0] for row in data) print 'SUM PLOT 2', sum(row[1] for row in data) print 'SUM PLOT 3', sum(row[2] for row in data) print data[0] p0 = plt.bar(index, data[0], width, color='y') # people p1 = plt.bar(index, data[1], width, color='g') # nature p2 = plt.bar(index, data[2], width, color='r') # activity p3 = plt.bar(index, data[3], width, color='b') # food p4 = plt.bar(index, data[4], width, color='c') # symbols p5 = plt.bar(index, data[5], width, color='m') # objects p6 = plt.bar(index, data[6], width, color='k') # flags p7 = plt.bar(index, data[7], width, color='w') # uncategorized plt.ylabel('Usage') plt.title('Emoji category usage per city') plt.xticks(index + width/2.0, cities) plt.yticks(np.arange(0, 1, 0.1)) plt.legend((p0[0], p1[0], p2[0], p3[0], p4[0], p5[0], p6[0], p7[0]), categories_names) plt.show()
def plot_gallery(images, titles, h, w, n_row=3,n_col=4): plt.figure(figsize=(1.8*n_col, 2.4*n_row)) plt.subplots_adjust(bottom=0,left=.01,right=.99,top=.90,hspace=.35) for i in range(n_row * n_col): plt.subplot(n_row,n_col,i+1) plt.imshow(images[i].reshape(h,w),cmap=plt.cm.gray) plt.title(titles[i],size=12) plt.xticks(()) plt.yticks(())
def plot_average(collected_results, versions, args, plot_std=True): test_type = args.test_type model_name = args.model means, stds = [], [] for version in versions: data = collected_results[version] if (plot_std): means.append(np.mean(data)) stds.append(np.std(data)) else: means.append(data) means = np.array(means) stds = np.array(stds) if (test_type == "size" or test_type == "allsize"): x = ["0%", "20%", "40%", "60%", "80%", "100%"] elif (test_type == "accdomain" or test_type == "moredomain"): x = [0, 1, 2, 3, 4] else: x = versions color = 'blue' plt.plot(x, means, color=color) if (plot_std): plt.fill_between(x, means - stds, means + stds, alpha=0.1, edgecolor=color, facecolor=color, linewidth=1, antialiased=True) plt.xticks(np.arange(len(x)), x, fontsize=18) plt.yticks(fontsize=18) plt.xlabel(XLABELS[test_type], fontsize=18) plt.ylabel('average absolute effect size', fontsize=18) plt.title("Influence of {} on bias removal \nfor {}".format( TITLES[test_type], MODEL_FORMAL_NAMES[model_name]), fontsize=18) plt.tight_layout() plot_path = os.path.join( args.eval_results_dir, "plots", "{}-{}-avg{}.png".format(model_name, test_type, "-std" if plot_std else "")) plt.savefig(plot_path)
def f3(): xs = [0, 1, 2, 5] ys = [0, 0, 1, 1] fig, ax = plt.subplots() ax.set_yticklabels([]) ax.set_xticklabels([]) plt.plot(xs, ys, label="$f_n(x)$") plt.xticks(xs, ['', r'$n-1$', r'$n$', '']) plt.yticks([1], ['$1$']) plt.legend() ax.spines['left'].set_position('zero') ax.spines['right'].set_color('none') ax.spines['bottom'].set_position('zero') ax.spines['top'].set_color('none') ax.axis('equal') plt.show()
def plot(self, ax=None): x = [*self.times, self.times[-1] + self.values[-1][1]] y = [*(v for v, d in self.values), self.values[-1][0]] from matplotlib.pylab import plt if ax is None: ax = plt.gca() ax.step(x, y, where='post') ax.set_xlabel('Time (seconds)') y_ticks = list(map(int, plt.yticks()[0])) ax.set_yticks(y_ticks, list(map(self.format_value.format, y_ticks)))
def tmp2(cm, acts): import numpy as np import matplotlib.pyplot as plt conf_arr = cm norm_conf = [] for i in conf_arr: a = 0 tmp_arr = [] a = sum(i, 0) for j in i: tmp_arr.append(0 if a == 0 else float(j) / float(a)) norm_conf.append(tmp_arr) fig = plt.figure(figsize=(8, 8)) plt.clf() ax = fig.add_subplot(111) ax.set_aspect(1) res = ax.imshow(np.array(norm_conf), cmap=plt.cm.jet, interpolation='nearest') width, height = conf_arr.shape # for x in range(width): # for y in range(height): # ax.annotate(str(conf_arr[x][y]), xy=(y, x), # horizontalalignment='center', # verticalalignment='center') cb = fig.colorbar(res) ax.set_xlim(-.5, len(acts) - .5) ax.set_ylim(-.5, len(acts) - .5) # alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' plt.xticks(range(width), acts, rotation=-90) plt.yticks(range(height), acts)
get_ipython().run_cell_magic( 'latex', '', '$\\textbf{Visualize the Correlations}: $\n$\\text{Cor}(X_i,Y_j) = \\frac{\\text{Cov}(X_i,Y_j)}{\\sigma_{X_i}\\sigma_{Y_j}}$' ) # In[101]: R = np.corrcoef(data.T) plt.figure(figsize=(10, 8)) plt.pcolor(R) plt.colorbar() plt.xlim([0, len(headers)]) plt.ylim([0, len(headers)]) plt.xticks(np.arange(32) + 0.5, np.array(headers), rotation='vertical') plt.yticks(np.arange(32) + 0.5, np.array(headers)) plt.show() # In[108]: #Lets fit both the models using PCA/FA down to two dimensions. #construct a function implementing the factor analysis which returns a vector of n_components largest # variances and the corresponding components (as column vectors in a matrix). You can # check your work by using decomposition.FactorAnalysis from sklearn #### ~THIS FUNCTION IS WAS A STAB, NEW CODE HERE: ########### def FactorAnalysis(data, n_components): ni = 20 data = data - data.mean(axis=0)