Python matplotlib.pyplot.matshow() Examples
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code examples of matplotlib.pyplot.matshow().
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Example #1
Source File: pearsons_filtering.py From simba with GNU Lesser General Public License v3.0 | 7 votes |
def pearson_filter(projectPath, featuresDf, del_corr_status, del_corr_threshold, del_corr_plot_status): print('Reducing features. Correlation threshold: ' + str(del_corr_threshold)) col_corr = set() corr_matrix = featuresDf.corr() for i in range(len(corr_matrix.columns)): for j in range(i): if (corr_matrix.iloc[i, j] >= del_corr_threshold) and (corr_matrix.columns[j] not in col_corr): colname = corr_matrix.columns[i] col_corr.add(colname) if colname in featuresDf.columns: del featuresDf[colname] if del_corr_plot_status == 'yes': print('Creating feature correlation heatmap...') dateTime = datetime.now().strftime('%Y%m%d%H%M%S') plt.matshow(featuresDf.corr()) plt.tight_layout() plt.savefig(os.path.join(projectPath, 'logs', 'Feature_correlations_' + dateTime + '.png'), dpi=300) plt.close('all') print('Feature correlation heatmap .png saved in project_folder/logs directory') return featuresDf
Example #2
Source File: denoising_autoencoder.py From Deep-Learning-with-TensorFlow-Second-Edition with MIT License | 6 votes |
def plotresult(org_vec,noisy_vec,out_vec): plt.matshow(np.reshape(org_vec, (28, 28)), cmap=plt.get_cmap('gray')) plt.title("Original Image") plt.colorbar() plt.matshow(np.reshape(noisy_vec, (28, 28)), cmap=plt.get_cmap('gray')) plt.title("Input Image") plt.colorbar() outimg = np.reshape(out_vec, (28, 28)) plt.matshow(outimg, cmap=plt.get_cmap('gray')) plt.title("Reconstructed Image") plt.colorbar() plt.show() # NETOWRK PARAMETERS
Example #3
Source File: deconvolutional_autoencoder.py From Deep-Learning-with-TensorFlow-Second-Edition with MIT License | 6 votes |
def plotresult(org_vec,noisy_vec,out_vec): plt.matshow(np.reshape(org_vec, (28, 28)), cmap=plt.get_cmap('gray')) plt.title("Original Image") plt.colorbar() plt.matshow(np.reshape(noisy_vec, (28, 28)), cmap=plt.get_cmap('gray')) plt.title("Input Image") plt.colorbar() outimg = np.reshape(out_vec, (28, 28)) plt.matshow(outimg, cmap=plt.get_cmap('gray')) plt.title("Reconstructed Image") plt.colorbar() plt.show() # NETOWORK PARAMETERS
Example #4
Source File: utils_visualization.py From deepwriting with MIT License | 6 votes |
def plot_matrix_and_get_image(plot_data, fig_height=8, fig_width=12, axis_off=False, colormap="jet"): fig = plt.figure() fig.set_figheight(fig_height) fig.set_figwidth(fig_width) plt.matshow(plot_data, fig.number) if fig_height < fig_width: plt.colorbar(orientation="horizontal") else: plt.colorbar(orientation="vertical") plt.set_cmap(colormap) if axis_off: plt.axis('off') img = fig_to_img(fig) plt.close(fig) return img
Example #5
Source File: plot_results.py From nasbench-1shot1 with Apache License 2.0 | 6 votes |
def plot_correlation_image(single_one_shot_training_database, epoch_idx=-1): correlation_matrix = np.zeros((3, 5)) for idx_cell, num_cells in enumerate([3, 6, 9]): for idx_ch, num_channels in enumerate([2, 4, 8, 16, 36]): config = single_one_shot_training_database.query( {'unrolled': False, 'cutout': False, 'search_space': '3', 'epochs': 50, 'init_channels': num_channels, 'weight_decay': 0.0003, 'warm_start_epochs': 0, 'learning_rate': 0.025, 'layers': num_cells}) if len(config) > 0: correlation = extract_correlation_per_epoch(config) correlation_matrix[idx_cell, idx_ch] = 1 - correlation[epoch_idx] plt.figure() plt.matshow(correlation_matrix) plt.xticks(np.arange(5), (2, 4, 8, 16, 36)) plt.yticks(np.arange(3), (3, 6, 9)) plt.colorbar() plt.savefig('test_correlation.png') plt.close() return correlation_matrix
Example #6
Source File: tf_input_data.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def MyPlot(cwtmatr): ''' 绘图 ''' print(type(cwtmatr)) print(len(cwtmatr)) print(len(cwtmatr[0])) # plt.plot(cwtmatr[1]) # plt.plot(cwtmatr[10]) # plt.plot(cwtmatr[100]) plt.plot(cwtmatr[1200]) plt.plot(cwtmatr[1210]) plt.plot(cwtmatr[1300]) plt.plot(cwtmatr[1400]) plt.plot(cwtmatr[1500]) # plt.plot(cwtmatr[1800]) # plt.plot(cwtmatr[1900]) # plt.plot(cwtmatr[2500]) # plt.matshow(cwtmatr) plt.show()
Example #7
Source File: interactive-eval.py From multiffn-nli with MIT License | 6 votes |
def plot_attention(tokens1, tokens2, attention): """ Print a colormap showing attention values from tokens 1 to tokens 2. """ len1 = len(tokens1) len2 = len(tokens2) extent = [0, len2, 0, len1] pl.matshow(attention, extent=extent, aspect='auto') ticks1 = np.arange(len1) + 0.5 ticks2 = np.arange(len2) + 0.5 pl.xticks(ticks2, tokens2, rotation=45) pl.yticks(ticks1, reversed(tokens1)) ax = pl.gca() ax.xaxis.set_ticks_position('bottom') pl.colorbar() pl.title('Alignments') pl.show(block=False)
Example #8
Source File: pursuit_evade.py From MADRL with MIT License | 6 votes |
def render(self, plt_delay=1.0): plt.matshow(self.model_state[0].T, cmap=plt.get_cmap('Greys'), fignum=1) for i in range(self.pursuer_layer.n_agents()): x, y = self.pursuer_layer.get_position(i) plt.plot(x, y, "r*", markersize=12) if self.train_pursuit: ax = plt.gca() ofst = self.obs_range / 2.0 ax.add_patch( Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5, facecolor="#FF9848")) for i in range(self.evader_layer.n_agents()): x, y = self.evader_layer.get_position(i) plt.plot(x, y, "b*", markersize=12) if not self.train_pursuit: ax = plt.gca() ofst = self.obs_range / 2.0 ax.add_patch( Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5, facecolor="#009ACD")) plt.pause(plt_delay) plt.clf()
Example #9
Source File: GetMLPara.py From dr_droid with Apache License 2.0 | 6 votes |
def draw_confusion_matrix(y_test, y_pred): from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred) print(cm) # Show confusion matrix in a separate window plt.matshow(cm) plt.title('Confusion matrix') plt.colorbar() plt.ylabel('True label') plt.xlabel('Predicted label') plt.show() ####################10 CV FALSE POSITIVE FLASE NEGATIVe#################################################
Example #10
Source File: deconvolutional_autoencoder_1.py From Deep-Learning-with-TensorFlow with MIT License | 6 votes |
def plotresult(org_vec,noisy_vec,out_vec): plt.matshow(np.reshape(org_vec, (28, 28)), cmap=plt.get_cmap('gray')) plt.title("Original Image") plt.colorbar() plt.matshow(np.reshape(noisy_vec, (28, 28)), cmap=plt.get_cmap('gray')) plt.title("Input Image") plt.colorbar() outimg = np.reshape(out_vec, (28, 28)) plt.matshow(outimg, cmap=plt.get_cmap('gray')) plt.title("Reconstructed Image") plt.colorbar() plt.show() # NETOWORK PARAMETERS
Example #11
Source File: denoising_autoencoder_1.py From Deep-Learning-with-TensorFlow with MIT License | 6 votes |
def plotresult(org_vec,noisy_vec,out_vec): plt.matshow(np.reshape(org_vec, (28, 28)), cmap=plt.get_cmap('gray')) plt.title("Original Image") plt.colorbar() plt.matshow(np.reshape(noisy_vec, (28, 28)), cmap=plt.get_cmap('gray')) plt.title("Input Image") plt.colorbar() outimg = np.reshape(out_vec, (28, 28)) plt.matshow(outimg, cmap=plt.get_cmap('gray')) plt.title("Reconstructed Image") plt.colorbar() plt.show() # NETOWRK PARAMETERS
Example #12
Source File: deconvolutional_autoencoder_1.py From Deep-Learning-with-TensorFlow with MIT License | 6 votes |
def plotresult(org_vec,noisy_vec,out_vec): plt.matshow(np.reshape(org_vec, (28, 28)),\ cmap=plt.get_cmap('gray')) plt.title("Original Image") plt.colorbar() plt.matshow(np.reshape(noisy_vec, (28, 28)),\ cmap=plt.get_cmap('gray')) plt.title("Input Image") plt.colorbar() outimg = np.reshape(out_vec, (28, 28)) plt.matshow(outimg, cmap=plt.get_cmap('gray')) plt.title("Reconstructed Image") plt.colorbar() plt.show() # NETOWORK PARAMETERS
Example #13
Source File: denoising_autoencoder_1.py From Deep-Learning-with-TensorFlow with MIT License | 6 votes |
def plotresult(org_vec,noisy_vec,out_vec): plt.matshow(np.reshape(org_vec, (28, 28)),\ cmap=plt.get_cmap('gray')) plt.title("Original Image") plt.colorbar() plt.matshow(np.reshape(noisy_vec, (28, 28)),\ cmap=plt.get_cmap('gray')) plt.title("Input Image") plt.colorbar() outimg = np.reshape(out_vec, (28, 28)) plt.matshow(outimg, cmap=plt.get_cmap('gray')) plt.title("Reconstructed Image") plt.colorbar() plt.show() # NETOWRK PARAMETERS
Example #14
Source File: visualization_utils.py From ludwig with Apache License 2.0 | 5 votes |
def confusion_matrix_plot( confusion_matrix, labels=None, output_feature_name=None, filename=None ): mpl.rcParams.update({'figure.autolayout': True}) fig, ax = plt.subplots() ax.invert_yaxis() ax.xaxis.tick_top() ax.xaxis.set_label_position('top') cax = ax.matshow(confusion_matrix, cmap='viridis') ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) ax.yaxis.set_major_locator(ticker.MultipleLocator(1)) ax.set_xticklabels([''] + labels, rotation=45, ha='left') ax.set_yticklabels([''] + labels) ax.grid(False) ax.tick_params(axis='both', which='both', length=0) fig.colorbar(cax, ax=ax, extend='max') ax.set_xlabel('Predicted {}'.format(output_feature_name)) ax.set_ylabel('Actual {}'.format(output_feature_name)) plt.tight_layout() ludwig.contrib.contrib_command("visualize_figure", plt.gcf()) if filename: plt.savefig(filename) else: plt.show()
Example #15
Source File: visualization_utils.py From ludwig with Apache License 2.0 | 5 votes |
def plot_matrix( matrix, cmap='hot', filename=None ): plt.matshow(matrix, cmap=cmap) ludwig.contrib.contrib_command("visualize_figure", plt.gcf()) if filename: plt.savefig(filename) else: plt.show()
Example #16
Source File: cnn_dogs_cats.py From Neural-Network-Programming-with-TensorFlow with MIT License | 5 votes |
def plot_confusion_matrix(cls_pred, data): # cls_pred is an array of the predicted class-number for # all images in the test-set. # Get the true classifications for the test-set. cls_true = data.valid.cls # Get the confusion matrix using sklearn. cm = confusion_matrix(y_true=cls_true, y_pred=cls_pred) # Print the confusion matrix as text. print(cm) # Plot the confusion matrix as an image. plt.matshow(cm) # Make various adjustments to the plot. plt.colorbar() tick_marks = np.arange(num_classes) plt.xticks(tick_marks, range(num_classes)) plt.yticks(tick_marks, range(num_classes)) plt.xlabel('Predicted') plt.ylabel('True') # Ensure the plot is shown correctly with multiple plots # in a single Notebook cell. plt.show()
Example #17
Source File: notebook.py From attention-lvcsr with MIT License | 5 votes |
def show_alignment(weights, transcription, bos_symbol=False, energies=None, **kwargs): f = pyplot.figure(figsize=(15, 0.20 * len(transcription))) ax = f.gca() ax.matshow(weights, aspect='auto', **kwargs) ax.set_yticks((1 if bos_symbol else 0) + numpy.arange(len(transcription))) ax.set_yticklabels(transcription) pyplot.show() if energies is not None: pyplot.matshow(energies, **kwargs) pyplot.colorbar() pyplot.show()
Example #18
Source File: testProject.py From FaceDetection with MIT License | 5 votes |
def func(): assert False pyplot.matshow(self.image) pylab.show()
Example #19
Source File: image.py From FaceDetection with MIT License | 5 votes |
def show(image = None): if image == None: return pyplot.matshow(image) pylab.show()
Example #20
Source File: tensorflow_input_data.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
def MyPlot(cwtmatr): ''' 绘图 ''' # print(type(cwtmatr)) # print(len(cwtmatr)) # print(len(cwtmatr[0])) # plt.plot(cwtmatr[1]) # plt.plot(cwtmatr[10]) # plt.plot(cwtmatr[200]) # plt.plot(cwtmatr[300]) # plt.plot(cwtmatr[400]) # plt.plot(cwtmatr[500]) # plt.plot(cwtmatr[600]) # plt.plot(cwtmatr[700]) # plt.plot(cwtmatr[1200]) # plt.plot(cwtmatr[1210]) # plt.plot(cwtmatr[1300]) # plt.plot(cwtmatr[1400]) # plt.plot(cwtmatr[1500]) # plt.plot(cwtmatr[1800]) # plt.plot(cwtmatr[1850]) # plt.plot(cwtmatr[1900]) # plt.plot(cwtmatr[1950]) # plt.plot(cwtmatr[2000]) # plt.plot(cwtmatr[2100]) # plt.plot(cwtmatr[2300]) # plt.plot(cwtmatr[2500]) # plt.matshow(cwtmatr) plt.show()
Example #21
Source File: tf_input_data.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
def MyPlot(cwtmatr): ''' 绘图 ''' print(type(cwtmatr)) print(len(cwtmatr)) print(len(cwtmatr[0])) # plt.plot(cwtmatr[1]) # plt.plot(cwtmatr[10]) # plt.plot(cwtmatr[200]) # plt.plot(cwtmatr[300]) # plt.plot(cwtmatr[400]) # plt.plot(cwtmatr[500]) # plt.plot(cwtmatr[600]) # plt.plot(cwtmatr[700]) # plt.plot(cwtmatr[1200]) # plt.plot(cwtmatr[1210]) # plt.plot(cwtmatr[1300]) # plt.plot(cwtmatr[1400]) # plt.plot(cwtmatr[1500]) # plt.plot(cwtmatr[1800]) # plt.plot(cwtmatr[1850]) # plt.plot(cwtmatr[1900]) # plt.plot(cwtmatr[1950]) # plt.plot(cwtmatr[2000]) # plt.plot(cwtmatr[2100]) # plt.plot(cwtmatr[2300]) # plt.plot(cwtmatr[2500]) # plt.matshow(cwtmatr) plt.show()
Example #22
Source File: tensorflow_input_data.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
def MyPlot(cwtmatr): ''' 绘图 ''' print(type(cwtmatr)) print(len(cwtmatr)) print(len(cwtmatr[0])) # plt.plot(cwtmatr[1]) # plt.plot(cwtmatr[10]) # plt.plot(cwtmatr[200]) # plt.plot(cwtmatr[300]) # plt.plot(cwtmatr[400]) # plt.plot(cwtmatr[500]) # plt.plot(cwtmatr[600]) # plt.plot(cwtmatr[700]) # plt.plot(cwtmatr[1200]) # plt.plot(cwtmatr[1210]) # plt.plot(cwtmatr[1300]) # plt.plot(cwtmatr[1400]) # plt.plot(cwtmatr[1500]) # plt.plot(cwtmatr[1800]) # plt.plot(cwtmatr[1850]) # plt.plot(cwtmatr[1900]) # plt.plot(cwtmatr[1950]) # plt.plot(cwtmatr[2000]) # plt.plot(cwtmatr[2100]) # plt.plot(cwtmatr[2300]) # plt.plot(cwtmatr[2500]) # plt.matshow(cwtmatr) plt.show()
Example #23
Source File: tensorflow_input_data.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
def MyPlot(cwtmatr): ''' 绘图 ''' # print(type(cwtmatr)) # print(len(cwtmatr)) # print(len(cwtmatr[0])) # plt.plot(cwtmatr[1]) # plt.plot(cwtmatr[10]) # plt.plot(cwtmatr[200]) # plt.plot(cwtmatr[300]) # plt.plot(cwtmatr[400]) # plt.plot(cwtmatr[500]) # plt.plot(cwtmatr[600]) # plt.plot(cwtmatr[700]) # plt.plot(cwtmatr[1200]) # plt.plot(cwtmatr[1210]) # plt.plot(cwtmatr[1300]) # plt.plot(cwtmatr[1400]) # plt.plot(cwtmatr[1500]) # plt.plot(cwtmatr[1800]) # plt.plot(cwtmatr[1850]) # plt.plot(cwtmatr[1900]) # plt.plot(cwtmatr[1950]) # plt.plot(cwtmatr[2000]) # plt.plot(cwtmatr[2100]) # plt.plot(cwtmatr[2300]) # plt.plot(cwtmatr[2500]) # plt.matshow(cwtmatr) plt.show()
Example #24
Source File: tensorflow_input_data.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
def MyPlot(cwtmatr): ''' 绘图 ''' # print(type(cwtmatr)) # print(len(cwtmatr)) # print(len(cwtmatr[0])) # plt.plot(cwtmatr[1]) # plt.plot(cwtmatr[10]) # plt.plot(cwtmatr[200]) # plt.plot(cwtmatr[300]) # plt.plot(cwtmatr[400]) # plt.plot(cwtmatr[500]) # plt.plot(cwtmatr[600]) # plt.plot(cwtmatr[700]) # plt.plot(cwtmatr[1200]) # plt.plot(cwtmatr[1210]) # plt.plot(cwtmatr[1300]) # plt.plot(cwtmatr[1400]) # plt.plot(cwtmatr[1500]) # plt.plot(cwtmatr[1800]) # plt.plot(cwtmatr[1850]) # plt.plot(cwtmatr[1900]) # plt.plot(cwtmatr[1950]) # plt.plot(cwtmatr[2000]) # plt.plot(cwtmatr[2100]) # plt.plot(cwtmatr[2300]) # plt.plot(cwtmatr[2500]) # plt.matshow(cwtmatr) plt.show()
Example #25
Source File: tensorflow_input_data.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
def MyPlot(cwtmatr): ''' 绘图 ''' # print(type(cwtmatr)) # print(len(cwtmatr)) # print(len(cwtmatr[0])) # plt.plot(cwtmatr[1]) # plt.plot(cwtmatr[10]) # plt.plot(cwtmatr[200]) # plt.plot(cwtmatr[300]) # plt.plot(cwtmatr[400]) # plt.plot(cwtmatr[500]) # plt.plot(cwtmatr[600]) # plt.plot(cwtmatr[700]) # plt.plot(cwtmatr[1200]) # plt.plot(cwtmatr[1210]) # plt.plot(cwtmatr[1300]) # plt.plot(cwtmatr[1400]) # plt.plot(cwtmatr[1500]) # plt.plot(cwtmatr[1800]) # plt.plot(cwtmatr[1850]) # plt.plot(cwtmatr[1900]) # plt.plot(cwtmatr[1950]) # plt.plot(cwtmatr[2000]) # plt.plot(cwtmatr[2100]) # plt.plot(cwtmatr[2300]) # plt.plot(cwtmatr[2500]) # plt.matshow(cwtmatr) plt.show()
Example #26
Source File: tensorflow_input_data.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
def MyPlot(cwtmatr): ''' 绘图 ''' # print(type(cwtmatr)) # print(len(cwtmatr)) # print(len(cwtmatr[0])) # plt.plot(cwtmatr[1]) # plt.plot(cwtmatr[10]) # plt.plot(cwtmatr[200]) # plt.plot(cwtmatr[300]) # plt.plot(cwtmatr[400]) # plt.plot(cwtmatr[500]) # plt.plot(cwtmatr[600]) # plt.plot(cwtmatr[700]) # plt.plot(cwtmatr[1200]) # plt.plot(cwtmatr[1210]) # plt.plot(cwtmatr[1300]) # plt.plot(cwtmatr[1400]) # plt.plot(cwtmatr[1500]) # plt.plot(cwtmatr[1800]) # plt.plot(cwtmatr[1850]) # plt.plot(cwtmatr[1900]) # plt.plot(cwtmatr[1950]) # plt.plot(cwtmatr[2000]) # plt.plot(cwtmatr[2100]) # plt.plot(cwtmatr[2300]) # plt.plot(cwtmatr[2500]) # plt.matshow(cwtmatr) plt.show()
Example #27
Source File: visualization.py From tf-image-segmentation with MIT License | 5 votes |
def _discrete_matshow_adaptive(data, labels_names=[], title=""): """Displays segmentation results using colormap that is adapted to a number of classes. Uses labels_names to write class names aside the color label. Used as a helper function for visualize_segmentation_adaptive() function. Parameters ---------- data : 2d numpy array (width, height) Array with integers representing class predictions labels_names : list List with class_names """ fig_size = [7, 6] plt.rcParams["figure.figsize"] = fig_size #get discrete colormap cmap = plt.get_cmap('Paired', np.max(data)-np.min(data)+1) # set limits .5 outside true range mat = plt.matshow(data, cmap=cmap, vmin = np.min(data)-.5, vmax = np.max(data)+.5) #tell the colorbar to tick at integers cax = plt.colorbar(mat, ticks=np.arange(np.min(data),np.max(data)+1)) # The names to be printed aside the colorbar if labels_names: cax.ax.set_yticklabels(labels_names) if title: plt.suptitle(title, fontsize=15, fontweight='bold') plt.show()
Example #28
Source File: plot_quasar_transform.py From 3DChromatin_ReplicateQC with MIT License | 5 votes |
def main(): parser = argparse.ArgumentParser(description='') parser.add_argument('--transform') parser.add_argument('--out') args = parser.parse_args() infile1 = h5py.File(args.transform, 'r') resolutions = infile1['resolutions'][...] chroms = infile1['chromosomes'][...] data1 = load_data(infile1, chroms, resolutions) infile1.close() ''' #for now, don't plot this for resolution in data1.keys(): for chromo in chroms: N = data1[resolution][chromo][1].shape[0] full=numpy.empty((N,N)) #full=full/0 for i in range(100): temp1 = numpy.arange(N - i - 1) temp2 = numpy.arange(i+1, N) full[temp1, temp2] = data1[resolution][chromo][1][temp1, i] full[temp2, temp1] = full[temp1, temp2] x=0.8 plt.matshow(full,cmap='seismic',vmin=-x,vmax=x) plt.colorbar() plt.show() plt.savefig(args.out+'.res'+str(resolution)+'.chr'+chromo+'.pdf') '''
Example #29
Source File: CNN_DogvsCat_Classifier.py From Practical-Convolutional-Neural-Networks with MIT License | 5 votes |
def plot_confusion_matrix(cls_pred): # cls_pred is an array of the predicted class-number for # all images in the test-set. # Get the true classifications for the test-set. cls_true = data.valid.cls # Get the confusion matrix using sklearn. cm = confusion_matrix(y_true=cls_true, y_pred=cls_pred) # Compute the precision, recall and f1 score of the classification p, r, f, s = precision_recall_fscore_support(cls_true, cls_pred, average='weighted') print('Precision:', p) print('Recall:', r) print('F1-score:', f) # Print the confusion matrix as text. print(cm) # Plot the confusion matrix as an image. plt.matshow(cm) # Make various adjustments to the plot. plt.colorbar() tick_marks = np.arange(num_classes) plt.xticks(tick_marks, range(num_classes)) plt.yticks(tick_marks, range(num_classes)) plt.xlabel('Predicted') plt.ylabel('True') # Ensure the plot is shown correctly with multiple plots # in a single Notebook cell. plt.show()
Example #30
Source File: pursuit_evade.py From MADRL with MIT License | 5 votes |
def save_image(self, file_name): plt.cla() plt.matshow(self.model_state[0].T, cmap=plt.get_cmap('Greys'), fignum=0) x, y = self.pursuer_layer.get_position(0) plt.plot(x, y, "r*", markersize=12) for i in range(self.pursuer_layer.n_agents()): x, y = self.pursuer_layer.get_position(i) plt.plot(x, y, "r*", markersize=12) if self.train_pursuit: ax = plt.gca() ofst = self.obs_range / 2.0 ax.add_patch( Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5, facecolor="#FF9848")) for i in range(self.evader_layer.n_agents()): x, y = self.evader_layer.get_position(i) plt.plot(x, y, "b*", markersize=12) if not self.train_pursuit: ax = plt.gca() ofst = self.obs_range / 2.0 ax.add_patch( Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5, facecolor="#009ACD")) xl, xh = -self.obs_offset - 1, self.xs + self.obs_offset + 1 yl, yh = -self.obs_offset - 1, self.ys + self.obs_offset + 1 plt.xlim([xl, xh]) plt.ylim([yl, yh]) plt.axis('off') plt.savefig(file_name, dpi=200)