def generate_plots(self, directory): # TODO: Enable plots for debug # helper.get_dataset_by_name(self.tb_data_handler.data, 'fm_demod')['result_okay'] = False # helper.get_dataset_by_name(self.tb_data_handler.data, 'fm_channel_data')['result_okay'] = False # helper.get_dataset_by_name(self.tb_data_handler.data, 'audio_mono')['result_okay'] = False # helper.get_dataset_by_name(self.tb_data_handler.data, 'pilot')['result_okay'] = False # helper.get_dataset_by_name(self.tb_data_handler.data, 'carrier_38k')['result_okay'] = False # helper.get_dataset_by_name(self.tb_data_handler.data, 'audio_lrdiff')['result_okay'] = False # helper.get_dataset_by_name(self.tb_data_handler.data, 'audio_L')['result_okay'] = False # helper.get_dataset_by_name(self.tb_data_handler.data, 'audio_R')['result_okay'] = False # Plot for i in range(0, len(self.model.data)): model_dataset = self.model.data[i] tb_dataset = self.tb_data_handler.data[i] self.log_info(f"Creating plot for {tb_dataset['name']}") tn = np.arange(0, len(tb_dataset['data'])) / tb_dataset['fs'] data_to_plot = ( (tn, tb_dataset['data'], "data_out_{}".format(tb_dataset['name'])), (tn, from_fixed_point(model_dataset['data']), "self.model.gold_{}".format(model_dataset['name'])) ) helper.plotData(data_to_plot, title=tb_dataset['name'], filename="{}/plot_{}.png".format(directory, tb_dataset['name']), show=not tb_dataset['result_okay'])
## =============== Part 1: Loading and Visualizing Data ================ # We start the exercise by first loading and visualizing the dataset. # The following code will load the dataset into your environment and plot # the data. # print('Loading and Visualizing Data ...') # Load from ex6data1: # You will have X, y in your environment mat = loadmat('ml/ex6/data/ex6data1.mat') X = mat["X"] y = mat["y"] plotData(X, y) input('Program paused. Press enter to continue.') ## ==================== Part 2: Training Linear SVM ==================== # The following code will train a linear SVM on the dataset and plot the # decision boundary learned. # # Load from ex6data1: # You will have X, y in your environment mat = loadmat('ml/ex6/data/ex6data1.mat') X = mat["X"] y = mat["y"] print('Training Linear SVM ...')
# Load Data # The first two columns contains the exam scores and the third column # contains the label. data = np.loadtxt('ml/ex2/ex2data1.txt', delimiter=",") X = data[:, :2] y = data[:, 2] ## ==================== Part 1: Plotting ==================== # We start the exercise by first plotting the data to understand the # the problem we are working with. print( 'Plotting data with + indicating (y = 1) examples and o indicating (y = 0) examples.' ) plt, p1, p2 = plotData(X, y) # # Labels and Legend plt.xlabel('Exam 1 score') plt.ylabel('Exam 2 score') plt.legend((p1, p2), ('Admitted', 'Not Admitted'), numpoints=1, handlelength=0) plt.show(block=False ) # prevents having to close the graph to move forward with ex2.py input('Program paused. Press enter to continue.\n') # plt.close() ## ============ Part 2: Compute Cost and Gradient ============ # In this part of the exercise, you will implement the cost and gradient # for logistic regression. You neeed to complete the code in
sigModelTPC = ROOT.RooGaussian('signal','signal function TPC', ns, muSigTPC, sigmaSigTPC) # bkg 1 nBkg0TPC = ROOT.RooRealVar(r'N_{bkg0}','counts TPC bkg0', 0, 1e+4) tauBkg0TPC = ROOT.RooRealVar(r'#tau_{bkg0}','tau TPC bkg0',-10, 10) bkg0ModelTPC = ROOT.RooExponential('bkg0','bkg0 function TPC',ns,tauBkg0TPC) nBkg1TPC = ROOT.RooRealVar(r'N_{bkg1}','counts TPC bkg1', 0, 1e+4) tauBkg1TPC = ROOT.RooRealVar(r'#tau_{bkg1}','tau TPC bkg1',-10, 10) bkg1ModelTPC = ROOT.RooExponential('bkg1','bkg1 function TPC',ns,tauBkg1TPC) totModelTPC = ROOT.RooAddPdf('model','tot function TPC', ROOT.RooArgList(sigModelTPC, bkg0ModelTPC, bkg1ModelTPC),ROOT.RooArgList(nSigTPC,nBkg0TPC, nBkg1TPC)) hTPChist = ROOT.RooDataHist(f"TPCsignalHe3_{he3_mom}", f"TPCsignalHe3_{he3_mom}", ROOT.RooArgList(ns), tpc_histo) frame, chi2,fitres = hp.plotData(ns, hTPChist, totModelTPC, 1.1, "SignalHe3", low_edge , sup_edge, f"SignalHe3_{he3_mom}") mu_list.append(muSigTPC.getVal()) mu_error_list.append(muSigTPC.getError()) sigma_list.append(sigmaSigTPC.getVal()) sigma_error_list.append(sigmaSigTPC.getError()) frame.Write() mean_histo = ROOT.TH1D(f"Mean TPCsignal", f"Mean TPCsignal", len(mom_values), mom_values[0] - 0.025, mom_values[-1] + 0.025) sigma_histo = ROOT.TH1D(f"Sigma TPCsignal", f"Sigma TPCsignal", len(mom_values), mom_values[0] - 0.025, mom_values[-1] + 0.025) for iBin in range(1, mean_histo.GetNbinsX() + 1): mean_histo.SetBinContent(iBin, mu_list[iBin - 1])