Python matplotlib.pyplot.matshow() Examples
The following are 30 code examples for showing how to use matplotlib.pyplot.matshow(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
| Project: simba Author: sgoldenlab File: pearsons_filtering.py License: GNU Lesser General Public License v3.0 | 6 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
| Project: Deep-Learning-with-TensorFlow-Second-Edition Author: PacktPublishing File: denoising_autoencoder.py License: 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
| Project: Deep-Learning-with-TensorFlow-Second-Edition Author: PacktPublishing File: deconvolutional_autoencoder.py License: 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
| Project: deepwriting Author: emreaksan File: utils_visualization.py License: 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
| Project: nasbench-1shot1 Author: automl File: plot_results.py License: 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
| Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: tf_input_data.py License: 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
| Project: multiffn-nli Author: erickrf File: interactive-eval.py License: 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
| Project: MADRL Author: sisl File: pursuit_evade.py License: 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
| Project: dr_droid Author: ririhedou File: GetMLPara.py License: 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
| Project: Deep-Learning-with-TensorFlow Author: PacktPublishing File: deconvolutional_autoencoder_1.py License: 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
| Project: Deep-Learning-with-TensorFlow Author: PacktPublishing File: denoising_autoencoder_1.py License: 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
| Project: Deep-Learning-with-TensorFlow Author: PacktPublishing File: deconvolutional_autoencoder_1.py License: 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
| Project: Deep-Learning-with-TensorFlow Author: PacktPublishing File: denoising_autoencoder_1.py License: 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
| Project: ludwig Author: uber File: visualization_utils.py License: 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
| Project: ludwig Author: uber File: visualization_utils.py License: 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
| Project: Neural-Network-Programming-with-TensorFlow Author: PacktPublishing File: cnn_dogs_cats.py License: 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
| Project: attention-lvcsr Author: rizar File: notebook.py License: 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
| Project: FaceDetection Author: jasonleaster File: testProject.py License: MIT License | 5 votes |
|
def func():
assert False
pyplot.matshow(self.image)
pylab.show()
Example 19
| Project: FaceDetection Author: jasonleaster File: image.py License: MIT License | 5 votes |
|
def show(image = None):
if image == None:
return
pyplot.matshow(image)
pylab.show()
Example 20
| Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: tensorflow_input_data.py License: 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
| Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: tf_input_data.py License: 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
| Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: tensorflow_input_data.py License: 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
| Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: tensorflow_input_data.py License: 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
| Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: tensorflow_input_data.py License: 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
| Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: tensorflow_input_data.py License: 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
| Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: tensorflow_input_data.py License: 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
| Project: tf-image-segmentation Author: warmspringwinds File: visualization.py License: 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
| Project: 3DChromatin_ReplicateQC Author: kundajelab File: plot_quasar_transform.py License: 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
| Project: Practical-Convolutional-Neural-Networks Author: PacktPublishing File: CNN_DogvsCat_Classifier.py License: 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
| Project: MADRL Author: sisl File: pursuit_evade.py License: 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)
