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Sourcery Starbot ⭐ refactored curenosm/MeIA #3

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Thanks for starring sourcery-ai/sourcery ✨ 🌟 ✨

Here's your pull request refactoring your most popular Python repo.

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To manually merge these changes, make sure you're on the develop branch, then run:

git fetch https://github.com/sourcery-ai-bot/MeIA develop
git merge --ff-only FETCH_HEAD
git reset HEAD^

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for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

for i, (train_index, test_index) in enumerate(skf.split(x, y)):

for train_index, test_index in skf.split(x, y):
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Function kfcv refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:


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Function losocv refactored with the following changes:

Comment on lines -34 to +43
print("Loading data from %s" % (data_file))
print(f"Loading data from {data_file}")

dataMat = scipy.io.loadmat(data_file, mat_dtype=True)
all_data = np.array(dataMat['features'])

data = np.array(all_data[:,:len(all_data[0])-1])
labels = np.array(all_data[:,len(all_data[0])-1])

print("Data loading complete. Shape is %r" % (dataMat['features'].shape,))

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Function load_data refactored with the following changes:

Comment on lines -34 to +43
print("Loading data from %s" % (data_file))
print(f"Loading data from {data_file}")

dataMat = scipy.io.loadmat(data_file, mat_dtype=True)
all_data = np.array(dataMat['features'])

data = np.array(all_data[:,:len(all_data[0])-1])
labels = np.array(all_data[:,len(all_data[0])-1])

print("Data loading complete. Shape is %r" % (dataMat['features'].shape,))

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Function load_data refactored with the following changes:

Comment on lines -281 to +308

"""
Module used to load dataset of bashivan et al. 2014.
"""

# load data pbashivan
data, labels = load_data(data_dir + "FeatureMat_timeWin.mat")
data, labels = load_data(f"{data_dir}FeatureMat_timeWin.mat")
print("Original data:",data.shape, labels.shape)

if generate_images:

# NOTE: Only a 3D projection is proporcionated, then it is not avaliable other
# records with positions.

#Load locations in 3D
locs_orig = scipy.io.loadmat(data_dir+'Neuroscan_locs_orig.mat', mat_dtype=True)
locs_orig = scipy.io.loadmat(
f'{data_dir}Neuroscan_locs_orig.mat', mat_dtype=True
)
locs3D = locs_orig['A']

#Convert to 2D
locs2D =[]
for e in locs3D:
locs2D.append(azim_proj(e))


locs2D = [azim_proj(e) for e in locs3D]
#save in numpy array
locs2D = np.array(locs2D)

# visualize projection
if visualize:
print("No. channels:",locs3D.shape)

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Function load_bashivan_data refactored with the following changes:

This removes the following comments ( why? ):

#Convert to 2D

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

for i, (train_index, test_index) in enumerate(skf.split(x, y)):

for train_index, test_index in skf.split(x, y):
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Function kfcv refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:


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Function losocv refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

Comment on lines -83 to +92

# Extract pairs between indexes and subjects
fold_pairs = get_subject_indices(subjects)

# Iterate over fold_pairs
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))

# Only Subjects 1, 2 are executed
if foldNum + 1 >= 3:# and foldNum + 1 <= 11:
if foldNum >= 2:# and foldNum + 1 <= 11:
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Function losocv refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

Comment on lines -86 to +96

# Extract pairs between indexes and subjects
fold_pairs = get_subject_indices(subjects)

# Iterate over fold_pairs
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))

# Only Subjects 1, 2 are executed
if foldNum + 1 >= 3:# and foldNum + 1 <= 11:
continue

if foldNum >= 2:# and foldNum + 1 <= 11:
continue

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Function losocv refactored with the following changes:

This removes the following comments ( why? ):

# Only Subjects 1, 2 are executed

Comment on lines -83 to +92

# Extract pairs between indexes and subjects
fold_pairs = get_subject_indices(subjects)

# Iterate over fold_pairs
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))

# Only Subjects 1, 2 are executed
if foldNum + 1 >= 3:# and foldNum + 1 <= 11:
if foldNum >= 2:# and foldNum + 1 <= 11:
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Function losocv refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

Comment on lines -86 to +96

# Extract pairs between indexes and subjects
fold_pairs = get_subject_indices(subjects)

# Iterate over fold_pairs
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))

# Only Subjects 1, 2 are executed
if foldNum + 1 >= 3:# and foldNum + 1 <= 11:
continue

if foldNum >= 2:# and foldNum + 1 <= 11:
continue

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Function losocv refactored with the following changes:

This removes the following comments ( why? ):

# Only Subjects 1, 2 are executed

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

Comment on lines -85 to +94

# Extract pairs between indexes and subjects
fold_pairs = get_subject_indices(subjects)

# Iterate over fold_pairs
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))

# Only Subjects 1, 2 are executed
if foldNum + 1 >= 3:# and foldNum + 1 <= 11:
if foldNum >= 2:# and foldNum + 1 <= 11:
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Function losocv refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

Comment on lines -85 to +94

# Extract pairs between indexes and subjects
fold_pairs = get_subject_indices(subjects)

# Iterate over fold_pairs
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))

# Only Subjects 1 and 2 are executed
if foldNum+1 >= 2:# and foldNum+1 <= 11:
if foldNum >= 1:# and foldNum+1 <= 11:
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Function losocv refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

Comment on lines -84 to +92

# Extract pairs between indexes and subjects
fold_pairs = get_subject_indices(subjects)

# Iterate over fold_pairs
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))

if foldNum+1 >= 3:# and foldNum+1 <= 11:
if foldNum >= 2:# and foldNum+1 <= 11:
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Function losocv refactored with the following changes:

out = self.fc_out(x)

return out
return self.fc_out(x)
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Function Predictor.forward refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

Comment on lines -85 to +94

# Extract pairs between indexes and subjects
fold_pairs = get_subject_indices(subjects)

# Iterate over fold_pairs
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))

# Only Subjects 1, 2 are executed
if foldNum + 1 >= 3:# and foldNum + 1 <= 11:
if foldNum >= 2:# and foldNum + 1 <= 11:
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Author

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Function losocv refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

Comment on lines -85 to +94

# Extract pairs between indexes and subjects
fold_pairs = get_subject_indices(subjects)

# Iterate over fold_pairs
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))

# Only Subjects 1 and 2 are executed
if foldNum+1 >= 2:# and foldNum+1 <= 11:
if foldNum >= 1:# and foldNum+1 <= 11:
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Author

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Function losocv refactored with the following changes:

for batch_idx, data in enumerate(test_loader):
for data in test_loader:
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Function test refactored with the following changes:

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