<commit_before>[DEFAULT]
repetitions = 1
iterations = 1000
seed = 42
compute_after = 0
compute_every = 100
compute_for = 10
compute_test_mode = False
path = results
num_cells = 20
num_epochs = 50
output_bias = False
num_predictions = 1
learning_window = float('inf')
separate_sequences_with = 'reset'
perturb_after = float('inf')
encoding_min = -1.0
encoding_max = 1.0
classify_with_random = True

[reber-basic]
encoding = 'basic'
encoding_num = 7
dataset = 'reber'
max_length = 100
num_predictions = 2
compute_every = 20
compute_for = 10
compute_test_mode = True

[reber-distributed]
encoding = 'distributed'
encoding_num = 100
dataset = 'reber'
max_length = 100
num_predictions = 2
compute_every = 100
compute_for = 50
compute_test_mode = True

[reber-distributed-random]
encoding = 'distributed'
encoding_num = 100
dataset = 'reber'
max_length = 100
num_predictions = 2
separate_sequences_with = 'random'
compute_every = 100
compute_for = 50
compute_test_mode = True

[simple-basic]
encoding = 'basic'
encoding_num = 10
dataset = 'simple'
compute_every = 200
compute_for = 100
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'reset'

[high-order-basic]
encoding = 'basic'
encoding_num = 10
dataset = 'high-order'
compute_every = 500
compute_for = 500
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'reset'
num_epochs = 250

[high-order-distributed]
encoding = 'distributed'
encoding_num = [10, 50]
dataset = 'high-order'
compute_every = 334
compute_for = 200
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'reset'
num_epochs = 200

[high-order-distributed-random]
encoding = 'distributed'
encoding_num = 10
classify_with_random = False
dataset = 'high-order'
compute_every = 500
compute_for = 500
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'random'
num_epochs = 500
learning_window = [3000, 9000]
iterations = 4999

[high-order-distributed-random-multiple-predictions]
encoding = 'distributed'
encoding_num = 25
classify_with_random = [False, True]
dataset = 'high-order'
compute_every = 1000
compute_for = 1000
compute_test_mode = True
num_predictions = 2
separate_sequences_with = 'random'
num_epochs = 500
learning_window = [6000, 9000, 12000]
iterations = 24999

[high-order-distributed-random-perturbed]
encoding = 'distributed'
encoding_num = 25
classify_with_random = [False, True]
dataset = 'high-order'
compute_every = 1000
compute_for = 1000
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'random'
perturb_after = [10000, 15000]
num_epochs = 500
learning_window = [3000, 6000, 9000]
iterations = 24999<commit_msg>Update experiment params<commit_after>[DEFAULT]
repetitions = 1
iterations = 1000
seed = 42
compute_after = 0
compute_every = 100
compute_for = 10
compute_test_mode = False
path = results
num_cells = 20
num_epochs = 50
output_bias = False
num_predictions = 1
learning_window = float('inf')
separate_sequences_with = 'reset'
perturb_after = float('inf')
encoding_min = -1.0
encoding_max = 1.0
classify_with_random = True

[reber-basic]
encoding = 'basic'
encoding_num = 7
dataset = 'reber'
max_length = 100
num_predictions = 2
compute_every = 20
compute_for = 10
compute_test_mode = True

[reber-distributed]
encoding = 'distributed'
encoding_num = 100
dataset = 'reber'
max_length = 100
num_predictions = 2
compute_every = 100
compute_for = 50
compute_test_mode = True

[reber-distributed-random]
encoding = 'distributed'
encoding_num = 100
dataset = 'reber'
max_length = 100
num_predictions = 2
separate_sequences_with = 'random'
compute_every = 100
compute_for = 50
compute_test_mode = True

[simple-basic]
encoding = 'basic'
encoding_num = 10
dataset = 'simple'
compute_every = 200
compute_for = 100
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'reset'

[high-order-basic]
encoding = 'basic'
encoding_num = 10
dataset = 'high-order'
compute_every = 500
compute_for = 500
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'reset'
num_epochs = 250

[high-order-distributed]
encoding = 'distributed'
encoding_num = [10, 50]
dataset = 'high-order'
compute_every = 334
compute_for = 200
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'reset'
num_epochs = 200

[high-order-distributed-random]
encoding = 'distributed'
encoding_num = 10
classify_with_random = False
dataset = 'high-order'
compute_every = 500
compute_for = 500
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'random'
num_epochs = 500
learning_window = [3000, 9000]
iterations = 4999

[high-order-distributed-random-multiple-predictions]
seed = [43, 44]
encoding = 'distributed'
encoding_num = 25
classify_with_random = [False, True]
dataset = 'high-order'
compute_every = 1000
compute_for = 1000
compute_test_mode = True
num_predictions = 2
separate_sequences_with = 'random'
num_epochs = 500
learning_window = [1000, 3000, 6000]
iterations = 14999

[high-order-distributed-random-perturbed]
seed = [43, 44]
encoding = 'distributed'
encoding_num = 25
classify_with_random = True
dataset = 'high-order'
compute_every = 1000
compute_for = 1000
compute_test_mode = True
num_predictions = 1
separate_sequences_with = 'random'
perturb_after = 10000
num_epochs = 500
learning_window = [1000, 3000, 6000]
iterations = 24999
{'fraction_non_alphanumeric': 0.09094325441563322, 'fraction_numerical': 0.07065013152950018, 'mean_word_length': 6.031704095112286, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6079419', 'n_tokens_mistral': 2166, 'n_tokens_neox': 1785, 'n_words': 467}