code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def __init__(self, app, *, options=None):
"""Initialize a new standalone application.
Args:
app: A wsgi Python application.
options (dict): the configuration.
"""
self.options = options or {}
self.application = app
super().__init__() | Initialize a new standalone application.
Args:
app: A wsgi Python application.
options (dict): the configuration.
| __init__ | python | bigchaindb/bigchaindb | bigchaindb/web/server.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/server.py | Apache-2.0 |
def create_app(*, debug=False, threads=1, bigchaindb_factory=None):
"""Return an instance of the Flask application.
Args:
debug (bool): a flag to activate the debug mode for the app
(default: False).
threads (int): number of threads to use
Return:
an instance of the Flas... | Return an instance of the Flask application.
Args:
debug (bool): a flag to activate the debug mode for the app
(default: False).
threads (int): number of threads to use
Return:
an instance of the Flask application.
| create_app | python | bigchaindb/bigchaindb | bigchaindb/web/server.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/server.py | Apache-2.0 |
def create_server(settings, log_config=None, bigchaindb_factory=None):
"""Wrap and return an application ready to be run.
Args:
settings (dict): a dictionary containing the settings, more info
here http://docs.gunicorn.org/en/latest/settings.html
Return:
an initialized instance... | Wrap and return an application ready to be run.
Args:
settings (dict): a dictionary containing the settings, more info
here http://docs.gunicorn.org/en/latest/settings.html
Return:
an initialized instance of the application.
| create_server | python | bigchaindb/bigchaindb | bigchaindb/web/server.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/server.py | Apache-2.0 |
def __call__(self, environ, start_response):
"""Run the middleware and then call the original WSGI application."""
if environ['REQUEST_METHOD'] == 'GET':
try:
del environ['CONTENT_TYPE']
except KeyError:
pass
else:
logg... | Run the middleware and then call the original WSGI application. | __call__ | python | bigchaindb/bigchaindb | bigchaindb/web/strip_content_type_middleware.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/strip_content_type_middleware.py | Apache-2.0 |
def _multiprocessing_to_asyncio(in_queue, out_queue, loop):
"""Bridge between a synchronous multiprocessing queue
and an asynchronous asyncio queue.
Args:
in_queue (multiprocessing.Queue): input queue
out_queue (asyncio.Queue): output queue
"""
while True:
value = in_queue.... | Bridge between a synchronous multiprocessing queue
and an asynchronous asyncio queue.
Args:
in_queue (multiprocessing.Queue): input queue
out_queue (asyncio.Queue): output queue
| _multiprocessing_to_asyncio | python | bigchaindb/bigchaindb | bigchaindb/web/websocket_server.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/websocket_server.py | Apache-2.0 |
def __init__(self, event_source):
"""Create a new instance.
Args:
event_source: a source of events. Elements in the queue
should be strings.
"""
self.event_source = event_source
self.subscribers = {} | Create a new instance.
Args:
event_source: a source of events. Elements in the queue
should be strings.
| __init__ | python | bigchaindb/bigchaindb | bigchaindb/web/websocket_server.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/websocket_server.py | Apache-2.0 |
async def publish(self):
"""Publish new events to the subscribers."""
while True:
event = await self.event_source.get()
str_buffer = []
if event == POISON_PILL:
return
if isinstance(event, str):
str_buffer.append(event)
... | Publish new events to the subscribers. | publish | python | bigchaindb/bigchaindb | bigchaindb/web/websocket_server.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/websocket_server.py | Apache-2.0 |
def init_app(event_source, *, loop=None):
"""Init the application server.
Return:
An aiohttp application.
"""
dispatcher = Dispatcher(event_source)
# Schedule the dispatcher
loop.create_task(dispatcher.publish())
app = web.Application(loop=loop)
app['dispatcher'] = dispatcher... | Init the application server.
Return:
An aiohttp application.
| init_app | python | bigchaindb/bigchaindb | bigchaindb/web/websocket_server.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/websocket_server.py | Apache-2.0 |
def start(sync_event_source, loop=None):
"""Create and start the WebSocket server."""
if not loop:
loop = asyncio.get_event_loop()
event_source = asyncio.Queue(loop=loop)
bridge = threading.Thread(target=_multiprocessing_to_asyncio,
args=(sync_event_source, event... | Create and start the WebSocket server. | start | python | bigchaindb/bigchaindb | bigchaindb/web/websocket_server.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/websocket_server.py | Apache-2.0 |
def base_ws_uri():
"""Base websocket URL that is advertised to external clients.
Useful when the websocket URL advertised to the clients needs to be
customized (typically when running behind NAT, firewall, etc.)
"""
config_wsserver = config['wsserver']
scheme = config_wsserver['advertised_sch... | Base websocket URL that is advertised to external clients.
Useful when the websocket URL advertised to the clients needs to be
customized (typically when running behind NAT, firewall, etc.)
| base_ws_uri | python | bigchaindb/bigchaindb | bigchaindb/web/views/base.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/views/base.py | Apache-2.0 |
def get(self, block_id):
"""API endpoint to get details about a block.
Args:
block_id (str): the id of the block.
Return:
A JSON string containing the data about the block.
"""
pool = current_app.config['bigchain_pool']
with pool() as bigchain:... | API endpoint to get details about a block.
Args:
block_id (str): the id of the block.
Return:
A JSON string containing the data about the block.
| get | python | bigchaindb/bigchaindb | bigchaindb/web/views/blocks.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/views/blocks.py | Apache-2.0 |
def get(self):
"""API endpoint to get the related blocks for a transaction.
Return:
A ``list`` of ``block_id``s that contain the given transaction. The
list may be filtered when provided a status query parameter:
"valid", "invalid", "undecided".
"""
p... | API endpoint to get the related blocks for a transaction.
Return:
A ``list`` of ``block_id``s that contain the given transaction. The
list may be filtered when provided a status query parameter:
"valid", "invalid", "undecided".
| get | python | bigchaindb/bigchaindb | bigchaindb/web/views/blocks.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/views/blocks.py | Apache-2.0 |
def get_api_v1_info(api_prefix):
"""Return a dict with all the information specific for the v1 of the
api.
"""
websocket_root = base_ws_uri() + EVENTS_ENDPOINT
docs_url = [
'https://docs.bigchaindb.com/projects/server/en/v',
version.__version__,
'/http-client-server-api.html'... | Return a dict with all the information specific for the v1 of the
api.
| get_api_v1_info | python | bigchaindb/bigchaindb | bigchaindb/web/views/info.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/views/info.py | Apache-2.0 |
def get(self):
"""API endpoint to retrieve a list of links to transaction
outputs.
Returns:
A :obj:`list` of :cls:`str` of links to outputs.
"""
parser = reqparse.RequestParser()
parser.add_argument('public_key', type=parameters.valid_ed25519,
... | API endpoint to retrieve a list of links to transaction
outputs.
Returns:
A :obj:`list` of :cls:`str` of links to outputs.
| get | python | bigchaindb/bigchaindb | bigchaindb/web/views/outputs.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/views/outputs.py | Apache-2.0 |
def get(self, tx_id):
"""API endpoint to get details about a transaction.
Args:
tx_id (str): the id of the transaction.
Return:
A JSON string containing the data about the transaction.
"""
pool = current_app.config['bigchain_pool']
with pool() a... | API endpoint to get details about a transaction.
Args:
tx_id (str): the id of the transaction.
Return:
A JSON string containing the data about the transaction.
| get | python | bigchaindb/bigchaindb | bigchaindb/web/views/transactions.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/views/transactions.py | Apache-2.0 |
def post(self):
"""API endpoint to push transactions to the Federation.
Return:
A ``dict`` containing the data about the transaction.
"""
parser = reqparse.RequestParser()
parser.add_argument('mode', type=parameters.valid_mode,
default=BRO... | API endpoint to push transactions to the Federation.
Return:
A ``dict`` containing the data about the transaction.
| post | python | bigchaindb/bigchaindb | bigchaindb/web/views/transactions.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/views/transactions.py | Apache-2.0 |
def get(self):
"""API endpoint to get validators set.
Return:
A JSON string containing the validator set of the current node.
"""
pool = current_app.config['bigchain_pool']
with pool() as bigchain:
validators = bigchain.get_validators()
return ... | API endpoint to get validators set.
Return:
A JSON string containing the validator set of the current node.
| get | python | bigchaindb/bigchaindb | bigchaindb/web/views/validators.py | https://github.com/bigchaindb/bigchaindb/blob/master/bigchaindb/web/views/validators.py | Apache-2.0 |
def generate_validators(powers):
"""Generates an arbitrary number of validators with random public keys.
The object under the `storage` key is in the format expected by DB.
The object under the `eleciton` key is in the format expected by
the upsert validator election.
`public_key`, `p... | Generates an arbitrary number of validators with random public keys.
The object under the `storage` key is in the format expected by DB.
The object under the `eleciton` key is in the format expected by
the upsert validator election.
`public_key`, `private_key` are in the format used for s... | generate_validators | python | bigchaindb/bigchaindb | tests/utils.py | https://github.com/bigchaindb/bigchaindb/blob/master/tests/utils.py | Apache-2.0 |
def _test_additionalproperties(node, path=''):
"""Validate that each object node has additionalProperties set, so that
objects with junk keys do not pass as valid.
"""
if isinstance(node, list):
for i, nnode in enumerate(node):
_test_additionalproperties(nnode, path + str(i) + '.')
... | Validate that each object node has additionalProperties set, so that
objects with junk keys do not pass as valid.
| _test_additionalproperties | python | bigchaindb/bigchaindb | tests/common/test_schema.py | https://github.com/bigchaindb/bigchaindb/blob/master/tests/common/test_schema.py | Apache-2.0 |
def test_cant_spend_same_input_twice_in_tx(b, alice):
"""Recreate duplicated fulfillments bug
https://github.com/bigchaindb/bigchaindb/issues/1099
"""
from bigchaindb.models import Transaction
from bigchaindb.common.exceptions import DoubleSpend
# create a divisible asset
tx_create = Transa... | Recreate duplicated fulfillments bug
https://github.com/bigchaindb/bigchaindb/issues/1099
| test_cant_spend_same_input_twice_in_tx | python | bigchaindb/bigchaindb | tests/db/test_bigchain_api.py | https://github.com/bigchaindb/bigchaindb/blob/master/tests/db/test_bigchain_api.py | Apache-2.0 |
def get_txs_patched(conn, **args):
"""Patch `get_transactions_filtered` so that rather than return an array
of transactions it returns an array of shims with a to_dict() method
that reports one of the arguments passed to `get_transactions_filtered`.
"""
return [type('... | Patch `get_transactions_filtered` so that rather than return an array
of transactions it returns an array of shims with a to_dict() method
that reports one of the arguments passed to `get_transactions_filtered`.
| get_txs_patched | python | bigchaindb/bigchaindb | tests/web/test_transactions.py | https://github.com/bigchaindb/bigchaindb/blob/master/tests/web/test_transactions.py | Apache-2.0 |
def call(self, x, training=None):
"""
Apply random channel-swap augmentation to `x`.
Args:
x (`Tensor`): A batch tensor of 1D (signals) or 2D (spectrograms) data
"""
if training in (None, False):
return x
# figure out input data format
if... |
Apply random channel-swap augmentation to `x`.
Args:
x (`Tensor`): A batch tensor of 1D (signals) or 2D (spectrograms) data
| call | python | keunwoochoi/kapre | kapre/augmentation.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/augmentation.py | MIT |
def _apply_masks_to_axis(self, x, axis, mask_param, n_masks):
"""
Applies a number of masks (defined by the parameter n_masks) to the spectrogram
by the axis provided.
Args:
x (float `Tensor`): A spectrogram. Its shape is (time, freq, ch) or (ch, time, freq)
... |
Applies a number of masks (defined by the parameter n_masks) to the spectrogram
by the axis provided.
Args:
x (float `Tensor`): A spectrogram. Its shape is (time, freq, ch) or (ch, time, freq)
depending on data_format.
axis (int): The axis where the m... | _apply_masks_to_axis | python | keunwoochoi/kapre | kapre/augmentation.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/augmentation.py | MIT |
def _apply_spec_augment(self, x):
"""
Main method that applies SpecAugment technique by both frequency and
time axis.
Args:
x (float `Tensor`) : A spectrogram. Its shape is (time, freq, ch) or (ch, time, freq)
depending on data_format.
Returns:
... |
Main method that applies SpecAugment technique by both frequency and
time axis.
Args:
x (float `Tensor`) : A spectrogram. Its shape is (time, freq, ch) or (ch, time, freq)
depending on data_format.
Returns:
(float `Tensor`): The spectrogram ma... | _apply_spec_augment | python | keunwoochoi/kapre | kapre/augmentation.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/augmentation.py | MIT |
def get_window_fn(window_name=None):
"""Return a window function given its name.
This function is used inside layers such as `STFT` to get a window function.
Args:
window_name (None or str): name of window function. On Tensorflow 2.3, there are five windows available in
`tf.signal` (`hammin... | Return a window function given its name.
This function is used inside layers such as `STFT` to get a window function.
Args:
window_name (None or str): name of window function. On Tensorflow 2.3, there are five windows available in
`tf.signal` (`hamming_window`, `hann_window`, `kaiser_bessel_der... | get_window_fn | python | keunwoochoi/kapre | kapre/backend.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/backend.py | MIT |
def validate_data_format_str(data_format):
"""A function that validates the data format string."""
if data_format not in (_CH_DEFAULT_STR, _CH_FIRST_STR, _CH_LAST_STR):
raise ValueError(
'data_format should be one of {}'.format(
str([_CH_FIRST_STR, _CH_LAST_STR, _CH_DEFAULT_S... | A function that validates the data format string. | validate_data_format_str | python | keunwoochoi/kapre | kapre/backend.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/backend.py | MIT |
def magnitude_to_decibel(x, ref_value=1.0, amin=1e-5, dynamic_range=80.0):
"""A function that converts magnitude to decibel scaling.
In essence, it runs `10 * log10(x)`, but with some other utility operations.
Similar to `librosa.power_to_db` with `ref=1.0` and `top_db=dynamic_range`
Args:
x (... | A function that converts magnitude to decibel scaling.
In essence, it runs `10 * log10(x)`, but with some other utility operations.
Similar to `librosa.power_to_db` with `ref=1.0` and `top_db=dynamic_range`
Args:
x (`Tensor`): float tensor. Can be batch or not. Something like magnitude of STFT.
... | magnitude_to_decibel | python | keunwoochoi/kapre | kapre/backend.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/backend.py | MIT |
def filterbank_mel(
sample_rate, n_freq, n_mels=128, f_min=0.0, f_max=None, htk=False, norm='slaney'
):
"""A wrapper for librosa.filters.mel that additionally does transpose and tensor conversion
Args:
sample_rate (`int`): sample rate of the input audio
n_freq (`int`): number of frequency b... | A wrapper for librosa.filters.mel that additionally does transpose and tensor conversion
Args:
sample_rate (`int`): sample rate of the input audio
n_freq (`int`): number of frequency bins in the input STFT magnitude.
n_mels (`int`): the number of mel bands
f_min (`float`): lowest fr... | filterbank_mel | python | keunwoochoi/kapre | kapre/backend.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/backend.py | MIT |
def get_stft_magnitude_layer(
input_shape=None,
n_fft=2048,
win_length=None,
hop_length=None,
window_name=None,
pad_begin=False,
pad_end=False,
return_decibel=False,
db_amin=1e-5,
db_ref_value=1.0,
db_dynamic_range=80.0,
input_data_format='default',
output_data_format... | A function that returns a stft magnitude layer.
The layer is a `keras.Sequential` model consists of `STFT`, `Magnitude`, and optionally `MagnitudeToDecibel`.
Args:
input_shape (None or tuple of integers): input shape of the model. Necessary only if this melspectrogram layer is
is the first ... | get_stft_magnitude_layer | python | keunwoochoi/kapre | kapre/composed.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/composed.py | MIT |
def get_melspectrogram_layer(
input_shape=None,
n_fft=2048,
win_length=None,
hop_length=None,
window_name=None,
pad_begin=False,
pad_end=False,
sample_rate=22050,
n_mels=128,
mel_f_min=0.0,
mel_f_max=None,
mel_htk=False,
mel_norm='slaney',
return_decibel=False,
... | A function that returns a melspectrogram layer, which is a `keras.Sequential` model consists of
`STFT`, `Magnitude`, `ApplyFilterbank(_mel_filterbank)`, and optionally `MagnitudeToDecibel`.
Args:
input_shape (None or tuple of integers): input shape of the model. Necessary only if this melspectrogram la... | get_melspectrogram_layer | python | keunwoochoi/kapre | kapre/composed.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/composed.py | MIT |
def get_log_frequency_spectrogram_layer(
input_shape=None,
n_fft=2048,
win_length=None,
hop_length=None,
window_name=None,
pad_begin=False,
pad_end=False,
sample_rate=22050,
log_n_bins=84,
log_f_min=None,
log_bins_per_octave=12,
log_spread=0.125,
return_decibel=False,... | A function that returns a log-frequency STFT layer, which is a `keras.Sequential` model consists of
`STFT`, `Magnitude`, `ApplyFilterbank(_log_filterbank)`, and optionally `MagnitudeToDecibel`.
Args:
input_shape (None or tuple of integers): input shape of the model if this melspectrogram layer is
... | get_log_frequency_spectrogram_layer | python | keunwoochoi/kapre | kapre/composed.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/composed.py | MIT |
def get_perfectly_reconstructing_stft_istft(
stft_input_shape=None,
istft_input_shape=None,
n_fft=2048,
win_length=None,
hop_length=None,
forward_window_name=None,
waveform_data_format='default',
stft_data_format='default',
stft_name='stft',
istft_name='istft',
):
"""A functi... | A function that returns two layers, stft and inverse stft, which would be perfectly reconstructing pair.
Args:
stft_input_shape (tuple): Input shape of single waveform.
Must specify this if the returned stft layer is going to be used as first layer of a Sequential model.
istft_input_sha... | get_perfectly_reconstructing_stft_istft | python | keunwoochoi/kapre | kapre/composed.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/composed.py | MIT |
def get_stft_mag_phase(
input_shape,
n_fft=2048,
win_length=None,
hop_length=None,
window_name=None,
pad_begin=False,
pad_end=False,
return_decibel=False,
db_amin=1e-5,
db_ref_value=1.0,
db_dynamic_range=80.0,
input_data_format='default',
output_data_format='default',... | A function that returns magnitude and phase of input audio.
Args:
input_shape (None or tuple of integers): input shape of the stft layer.
Because this mag_phase is based on keras.Functional model, it is required to specify the input shape.
E.g., (44100, 2) for 44100-sample stereo au... | get_stft_mag_phase | python | keunwoochoi/kapre | kapre/composed.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/composed.py | MIT |
def get_frequency_aware_conv2d(
data_format='default', freq_aware_name='frequency_aware_conv2d', *args, **kwargs
):
"""Returns a frequency-aware conv2d layer.
Args:
data_format (str): specifies the data format of batch input/output.
freq_aware_name (str): name of the returned layer
... | Returns a frequency-aware conv2d layer.
Args:
data_format (str): specifies the data format of batch input/output.
freq_aware_name (str): name of the returned layer
*args: position args for `keras.layers.Conv2D`.
**kwargs: keyword args for `keras.layers.Conv2D`.
Returns:
... | get_frequency_aware_conv2d | python | keunwoochoi/kapre | kapre/composed.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/composed.py | MIT |
def call(self, x):
"""
Args:
x (`Tensor`): batch audio signal in the specified 1D format in initiation.
Returns:
(`Tensor`): A framed tensor. The shape is (batch, time (frames), frame_length, channel) if `channels_last`,
or (batch, channel, time (frames), fra... |
Args:
x (`Tensor`): batch audio signal in the specified 1D format in initiation.
Returns:
(`Tensor`): A framed tensor. The shape is (batch, time (frames), frame_length, channel) if `channels_last`,
or (batch, channel, time (frames), frame_length) if `channels_first`... | call | python | keunwoochoi/kapre | kapre/signal.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/signal.py | MIT |
def call(self, x):
"""
Args:
x (`Tensor`): batch audio signal in the specified 1D format in initiation.
Returns:
(`Tensor`): A framed tensor. The shape is (batch, time (frames), channel) if `channels_last`, or
(batch, channel, time (frames)) if `channels_firs... |
Args:
x (`Tensor`): batch audio signal in the specified 1D format in initiation.
Returns:
(`Tensor`): A framed tensor. The shape is (batch, time (frames), channel) if `channels_last`, or
(batch, channel, time (frames)) if `channels_first`.
| call | python | keunwoochoi/kapre | kapre/signal.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/signal.py | MIT |
def call(self, log_melgrams):
"""
Args:
log_melgrams (float `Tensor`): a batch of log_melgrams. `(b, time, mel, ch)` if `channels_last`
and `(b, ch, time, mel)` if `channels_first`.
Returns:
(float `Tensor`):
MFCCs. `(batch, time, n_mfccs, ch... |
Args:
log_melgrams (float `Tensor`): a batch of log_melgrams. `(b, time, mel, ch)` if `channels_last`
and `(b, ch, time, mel)` if `channels_first`.
Returns:
(float `Tensor`):
MFCCs. `(batch, time, n_mfccs, ch)` if `channels_last`, `(batch, ch, time,... | call | python | keunwoochoi/kapre | kapre/signal.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/signal.py | MIT |
def _rdft(signal, dft_length):
"""DFT for real signals.
Calculates the onesided dft, assuming real signal implies complex conjugate symetry,
hence only onesided DFT is returned.
Args:
signal (tensor) signal to transform, assumes that the last dimension is the time dimension
signal c... | DFT for real signals.
Calculates the onesided dft, assuming real signal implies complex conjugate symetry,
hence only onesided DFT is returned.
Args:
signal (tensor) signal to transform, assumes that the last dimension is the time dimension
signal can be framed, e.g. (1, 40, 1024) for a... | _rdft | python | keunwoochoi/kapre | kapre/tflite_compatible_stft.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/tflite_compatible_stft.py | MIT |
def fixed_frame(signal, frame_length, frame_step):
"""tflite-compatible tf.signal.frame for fixed-size input.
Args:
signal: Tensor containing signal(s).
frame_length: Number of samples to put in each frame.
frame_step: Sample advance between successive frames.
Returns:
A ne... | tflite-compatible tf.signal.frame for fixed-size input.
Args:
signal: Tensor containing signal(s).
frame_length: Number of samples to put in each frame.
frame_step: Sample advance between successive frames.
Returns:
A new tensor where the last axis (or first, if first_axis) of ... | fixed_frame | python | keunwoochoi/kapre | kapre/tflite_compatible_stft.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/tflite_compatible_stft.py | MIT |
def stft_tflite(signal, frame_length, frame_step, fft_length, window_fn, pad_end):
"""tflite-compatible implementation of tf.signal.stft.
Compute the short-time Fourier transform of a 1D input while avoiding tf ops
that are not currently supported in tflite (Rfft, Range, SplitV).
fft_length must be fixe... | tflite-compatible implementation of tf.signal.stft.
Compute the short-time Fourier transform of a 1D input while avoiding tf ops
that are not currently supported in tflite (Rfft, Range, SplitV).
fft_length must be fixed. A Hann window is of frame_length is always
applied.
Since fixed (precomputed) f... | stft_tflite | python | keunwoochoi/kapre | kapre/tflite_compatible_stft.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/tflite_compatible_stft.py | MIT |
def continued_fraction_arctan(x, n=100, dtype=tf.float32):
"""Continued fraction Approximation to the arctan function
Approximate solution to arctan(x), atan is not a natively supported tflite
op (or a flex op). n is the number of iterations, the high the more accurate.
Accuracy is poor whe... | Continued fraction Approximation to the arctan function
Approximate solution to arctan(x), atan is not a natively supported tflite
op (or a flex op). n is the number of iterations, the high the more accurate.
Accuracy is poor when the argument is large.
https://functions.wolfram.com/Ele... | continued_fraction_arctan | python | keunwoochoi/kapre | kapre/tflite_compatible_stft.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/tflite_compatible_stft.py | MIT |
def atan2_tflite(y, x, n=100, dtype=tf.float32):
"""Approximation to the atan2 function
atan is not a tflite supported op or flex op, thus this uses an Approximation
Poor accuracy when either x is very small or y is very large.
https://en.wikipedia.org/wiki/Atan2
Args:
y (tenso... | Approximation to the atan2 function
atan is not a tflite supported op or flex op, thus this uses an Approximation
Poor accuracy when either x is very small or y is very large.
https://en.wikipedia.org/wiki/Atan2
Args:
y (tensor) - vertical component of tangent (or imaginary part of... | atan2_tflite | python | keunwoochoi/kapre | kapre/tflite_compatible_stft.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/tflite_compatible_stft.py | MIT |
def _shape_spectrum_output(spectrums, data_format):
"""Shape batch spectrograms into the right format.
Args:
spectrums (`Tensor`): result of tf.signal.stft or similar, i.e., (..., time, freq).
data_format (`str`): 'channels_first' or 'channels_last'
Returns:
spectrums (`Tensor`): a... | Shape batch spectrograms into the right format.
Args:
spectrums (`Tensor`): result of tf.signal.stft or similar, i.e., (..., time, freq).
data_format (`str`): 'channels_first' or 'channels_last'
Returns:
spectrums (`Tensor`): a transposed version of input `spectrums`
| _shape_spectrum_output | python | keunwoochoi/kapre | kapre/time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/time_frequency.py | MIT |
def call(self, x):
"""
Compute STFT of the input signal. If the `time` axis is not the last axis of `x`, it should be transposed first.
Args:
x (float `Tensor`): batch of audio signals, (batch, ch, time) or (batch, time, ch) based on input_data_format
Return:
(c... |
Compute STFT of the input signal. If the `time` axis is not the last axis of `x`, it should be transposed first.
Args:
x (float `Tensor`): batch of audio signals, (batch, ch, time) or (batch, time, ch) based on input_data_format
Return:
(complex `Tensor`): A STFT repre... | call | python | keunwoochoi/kapre | kapre/time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/time_frequency.py | MIT |
def call(self, x):
"""
Compute inverse STFT of the input STFT.
Args:
x (complex `Tensor`): batch of STFTs, (batch, ch, time, freq) or (batch, time, freq, ch) depending on `input_data_format`
Return:
(`float`): audio signals of x. Shape: 1D batch shape. I.e., (ba... |
Compute inverse STFT of the input STFT.
Args:
x (complex `Tensor`): batch of STFTs, (batch, ch, time, freq) or (batch, time, freq, ch) depending on `input_data_format`
Return:
(`float`): audio signals of x. Shape: 1D batch shape. I.e., (batch, time, ch) or (batch, ch, ... | call | python | keunwoochoi/kapre | kapre/time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/time_frequency.py | MIT |
def call(self, x):
"""
Args:
x (complex `Tensor`): input complex tensor
Returns:
(float `Tensor`): phase of `x` (Radian)
"""
if self.approx_atan_accuracy:
return atan2_tflite(tf.math.imag(x), tf.math.real(x), n=self.approx_atan_accuracy)
... |
Args:
x (complex `Tensor`): input complex tensor
Returns:
(float `Tensor`): phase of `x` (Radian)
| call | python | keunwoochoi/kapre | kapre/time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/time_frequency.py | MIT |
def call(self, x):
"""
Args:
x (`Tensor`): float tensor. Can be batch or not. Something like magnitude of STFT.
Returns:
(`Tensor`): decibel-scaled float tensor of `x`.
"""
return backend.magnitude_to_decibel(
x, ref_value=self.ref_value, amin... |
Args:
x (`Tensor`): float tensor. Can be batch or not. Something like magnitude of STFT.
Returns:
(`Tensor`): decibel-scaled float tensor of `x`.
| call | python | keunwoochoi/kapre | kapre/time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/time_frequency.py | MIT |
def call(self, x):
"""
Apply filterbank to `x`.
Args:
x (`Tensor`): float tensor in 2D batch shape.
"""
# x: 2d batch input. (b, t, fr, ch) or (b, ch, t, fr)
output = tf.tensordot(x, self.filterbank, axes=(self.freq_axis, 0))
# ch_last -> (b, t, ch, ... |
Apply filterbank to `x`.
Args:
x (`Tensor`): float tensor in 2D batch shape.
| call | python | keunwoochoi/kapre | kapre/time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/time_frequency.py | MIT |
def call(self, x):
"""
Args:
x (`Tensor`): a 2d batch (b, t, f, ch) or (b, ch, t, f)
Returns:
(`Tensor`): A tensor with the same shape as input data.
"""
if self.data_format == 'channels_first':
x = K.permute_dimensions(x, (0, 2, 3, 1))
... |
Args:
x (`Tensor`): a 2d batch (b, t, f, ch) or (b, ch, t, f)
Returns:
(`Tensor`): A tensor with the same shape as input data.
| call | python | keunwoochoi/kapre | kapre/time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/time_frequency.py | MIT |
def call(self, x):
"""
Compute STFT of the input signal. If the `time` axis is not the last axis of `x`, it should be transposed first.
Args:
x (float `Tensor`): batch of audio signals, (batch, ch, time) or (batch, time, ch) based on input_data_format
Return:
(r... |
Compute STFT of the input signal. If the `time` axis is not the last axis of `x`, it should be transposed first.
Args:
x (float `Tensor`): batch of audio signals, (batch, ch, time) or (batch, time, ch) based on input_data_format
Return:
(real `Tensor`): A STFT represen... | call | python | keunwoochoi/kapre | kapre/time_frequency_tflite.py | https://github.com/keunwoochoi/kapre/blob/master/kapre/time_frequency_tflite.py | MIT |
def test_spec_augment_apply_masks_to_axis(inputs):
"""
Tests the method _apply_masks_to_axis to see if shape is kept and
exceptions are caught
"""
data_format, axis, mask_param, n_masks = inputs
batch_src, input_shape = get_spectrogram(data_format)
spec_augment = SpecAugment(
input... |
Tests the method _apply_masks_to_axis to see if shape is kept and
exceptions are caught
| test_spec_augment_apply_masks_to_axis | python | keunwoochoi/kapre | tests/test_augmentation.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_augmentation.py | MIT |
def test_spec_augment_depth_exception():
"""
Checks that SpecAugments fails if Spectrogram has depth greater than 1.
"""
data_format = "default"
with pytest.raises(RuntimeError):
batch_src, input_shape = get_spectrogram(data_format=data_format, n_ch=4)
model = tf.keras.Sequential(... |
Checks that SpecAugments fails if Spectrogram has depth greater than 1.
| test_spec_augment_depth_exception | python | keunwoochoi/kapre | tests/test_augmentation.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_augmentation.py | MIT |
def test_spec_augment_layer(data_format, atol=1e-4):
"""
Tests the complete layer, checking if the parameter `training` has the expected behaviour.
"""
batch_src, input_shape = get_spectrogram(data_format)
model = tf.keras.Sequential()
spec_augment = SpecAugment(
input_shape=input_shap... |
Tests the complete layer, checking if the parameter `training` has the expected behaviour.
| test_spec_augment_layer | python | keunwoochoi/kapre | tests/test_augmentation.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_augmentation.py | MIT |
def test_filterbank_log(sample_rate, n_freq, n_bins, bins_per_octave, f_min, spread):
"""It only tests if the function is a valid wrapper"""
log_fb = KPB.filterbank_log(
sample_rate=sample_rate,
n_freq=n_freq,
n_bins=n_bins,
bins_per_octave=bins_per_octave,
f_min=f_min,
... | It only tests if the function is a valid wrapper | test_filterbank_log | python | keunwoochoi/kapre | tests/test_backend.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_backend.py | MIT |
def allclose_phase(a, b, atol=1e-3):
"""Testing phase.
Remember that a small error in complex value may lead to a large phase difference
if the norm is very small.
Therefore, it makes more sense to test it on the complex value itself rather than breaking it down to phase.
"""
np.testing.assert... | Testing phase.
Remember that a small error in complex value may lead to a large phase difference
if the norm is very small.
Therefore, it makes more sense to test it on the complex value itself rather than breaking it down to phase.
| allclose_phase | python | keunwoochoi/kapre | tests/test_time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_time_frequency.py | MIT |
def assert_approx_phase(a, b, atol=1e-2, acceptable_fail_ratio=0.01):
"""Testing approximate phase.
Tflite phase is approximate, some values will always have a large error
So makes more sense to count the number that are within tolerance
"""
count_failed = np.sum(np.abs(a - b) > atol)
assert (
... | Testing approximate phase.
Tflite phase is approximate, some values will always have a large error
So makes more sense to count the number that are within tolerance
| assert_approx_phase | python | keunwoochoi/kapre | tests/test_time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_time_frequency.py | MIT |
def test_melspectrogram_correctness(
n_fft, sr, hop_length, n_ch, data_format, amin, dynamic_range, n_mels, mel_f_min, mel_f_max
):
"""Test the correctness of melspectrogram.
Note that mel filterbank is tested separated
"""
def _get_melgram_model(return_decibel, amin, dynamic_range, input_shape=N... | Test the correctness of melspectrogram.
Note that mel filterbank is tested separated
| test_melspectrogram_correctness | python | keunwoochoi/kapre | tests/test_time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_time_frequency.py | MIT |
def test_log_spectrogram_runnable(data_format):
"""test if log spectrogram layer works well"""
src_mono, batch_src, input_shape = get_audio(data_format=data_format, n_ch=1)
_ = get_log_frequency_spectrogram_layer(input_shape, return_decibel=True)
_ = get_log_frequency_spectrogram_layer(input_shape, retu... | test if log spectrogram layer works well | test_log_spectrogram_runnable | python | keunwoochoi/kapre | tests/test_time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_time_frequency.py | MIT |
def test_log_spectrogram_fail():
"""test if log spectrogram layer works well"""
src_mono, batch_src, input_shape = get_audio(data_format='channels_last', n_ch=1)
_ = get_log_frequency_spectrogram_layer(input_shape, return_decibel=True, log_n_bins=200) | test if log spectrogram layer works well | test_log_spectrogram_fail | python | keunwoochoi/kapre | tests/test_time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_time_frequency.py | MIT |
def test_save_load(save_format):
"""test saving/loading of models that has stft, melspectorgrma, and log frequency."""
src_mono, batch_src, input_shape = get_audio(data_format='channels_last', n_ch=1)
# test STFT save/load
save_load_compare(
STFT(input_shape=input_shape, pad_begin=True),
... | test saving/loading of models that has stft, melspectorgrma, and log frequency. | test_save_load | python | keunwoochoi/kapre | tests/test_time_frequency.py | https://github.com/keunwoochoi/kapre/blob/master/tests/test_time_frequency.py | MIT |
def save_load_compare(
layer, input_batch, allclose_func, save_format, layer_class=None, training=None, atol=1e-4
):
"""test a model with `layer` with the given `input_batch`.
The model prediction result is compared using `allclose_func` which may depend on the
data type of the model output (e.g., float... | test a model with `layer` with the given `input_batch`.
The model prediction result is compared using `allclose_func` which may depend on the
data type of the model output (e.g., float or complex).
| save_load_compare | python | keunwoochoi/kapre | tests/utils.py | https://github.com/keunwoochoi/kapre/blob/master/tests/utils.py | MIT |
def predict_using_tflite(model, batch_src):
"""Convert a keras model to tflite and infer on batch_src
Attempts to convert a keras model to a tflite model, load the tflite model,
then infer on the data in batch_src
Args:
model (keras model)
batch_src (numpy array) - audio to test model
... | Convert a keras model to tflite and infer on batch_src
Attempts to convert a keras model to a tflite model, load the tflite model,
then infer on the data in batch_src
Args:
model (keras model)
batch_src (numpy array) - audio to test model
Returns:
pred_tflite (numpy array) - arr... | predict_using_tflite | python | keunwoochoi/kapre | tests/utils.py | https://github.com/keunwoochoi/kapre/blob/master/tests/utils.py | MIT |
def add(ctx, task, priority, tags, extra, category, labels):
"""Add a new task to the to-do list.
Note:
Control the output of this using the verbosity option.
"""
if ctx.obj["verbose"] >= 2:
click.echo(f"Adding task: {task}")
click.echo(f"Priority: {priority}")
click.echo(f'T... | Add a new task to the to-do list.
Note:
Control the output of this using the verbosity option.
| add | python | Textualize/trogon | examples/demo.py | https://github.com/Textualize/trogon/blob/master/examples/demo.py | MIT |
def remove(ctx, task_id):
"""Remove a task from the to-do list by its ID."""
if ctx.obj["verbose"] >= 1:
click.echo(f"Removing task with ID: {task_id}")
# Implement the task removal functionality here | Remove a task from the to-do list by its ID. | remove | python | Textualize/trogon | examples/demo.py | https://github.com/Textualize/trogon/blob/master/examples/demo.py | MIT |
def list_tasks(ctx, all, completed):
"""List tasks from the to-do list."""
if ctx.obj["verbose"] >= 1:
click.echo(f"Listing tasks:")
# Implement the task listing functionality here | List tasks from the to-do list. | list_tasks | python | Textualize/trogon | examples/demo.py | https://github.com/Textualize/trogon/blob/master/examples/demo.py | MIT |
def add(verbose, task, priority, tags, extra, category, labels):
"""Add a new task to the to-do list."""
if verbose >= 2:
click.echo(f"Adding task: {task}")
click.echo(f"Priority: {priority}")
click.echo(f'Tags: {", ".join(tags)}')
click.echo(f"Extra data: {extra}")
click... | Add a new task to the to-do list. | add | python | Textualize/trogon | examples/nogroup_demo.py | https://github.com/Textualize/trogon/blob/master/examples/nogroup_demo.py | MIT |
def detect_run_string(_main: ModuleType = sys.modules["__main__"]) -> str:
"""This is a slightly modified version of a function from Click."""
path = sys.argv[0]
# The value of __package__ indicates how Python was called. It may
# not exist if a setuptools script is installed as an egg. It may be
#... | This is a slightly modified version of a function from Click. | detect_run_string | python | Textualize/trogon | trogon/detect_run_string.py | https://github.com/Textualize/trogon/blob/master/trogon/detect_run_string.py | MIT |
def introspect_click_app(app: BaseCommand) -> dict[CommandName, CommandSchema]:
"""
Introspect a Click application and build a data structure containing
information about all commands, options, arguments, and subcommands,
including the docstrings and command function references.
This function recur... |
Introspect a Click application and build a data structure containing
information about all commands, options, arguments, and subcommands,
including the docstrings and command function references.
This function recursively processes each command and its subcommands
(if any), creating a nested dicti... | introspect_click_app | python | Textualize/trogon | trogon/introspect.py | https://github.com/Textualize/trogon/blob/master/trogon/introspect.py | MIT |
def to_cli_args(self, include_root_command: bool = False) -> list[str]:
"""
Generates a list of strings representing the CLI invocation based on the user input data.
Returns:
A list of strings that can be passed to subprocess.run to execute the command.
"""
cli_args ... |
Generates a list of strings representing the CLI invocation based on the user input data.
Returns:
A list of strings that can be passed to subprocess.run to execute the command.
| to_cli_args | python | Textualize/trogon | trogon/run_command.py | https://github.com/Textualize/trogon/blob/master/trogon/run_command.py | MIT |
def to_cli_string(self, include_root_command: bool = False) -> Text:
"""
Generates a string representing the CLI invocation as if typed directly into the
command line.
Returns:
A string representing the command invocation.
"""
args = self.to_cli_args(include_... |
Generates a string representing the CLI invocation as if typed directly into the
command line.
Returns:
A string representing the command invocation.
| to_cli_string | python | Textualize/trogon | trogon/run_command.py | https://github.com/Textualize/trogon/blob/master/trogon/run_command.py | MIT |
async def selected_command_changed(
self, event: Tree.NodeHighlighted[CommandSchema]
) -> None:
"""When we highlight a node in the CommandTree, the main body of the home page updates
to display a form specific to the highlighted command."""
await self._refresh_command_form(event.node... | When we highlight a node in the CommandTree, the main body of the home page updates
to display a form specific to the highlighted command. | selected_command_changed | python | Textualize/trogon | trogon/trogon.py | https://github.com/Textualize/trogon/blob/master/trogon/trogon.py | MIT |
def _update_command_description(self, command: CommandSchema) -> None:
"""Update the description of the command at the bottom of the sidebar
based on the currently selected node in the command tree."""
description_box = self.query_one("#home-command-description", Static)
description_text... | Update the description of the command at the bottom of the sidebar
based on the currently selected node in the command tree. | _update_command_description | python | Textualize/trogon | trogon/trogon.py | https://github.com/Textualize/trogon/blob/master/trogon/trogon.py | MIT |
def _update_execution_string_preview(self) -> None:
"""Update the preview box showing the command string to be executed"""
command_name_syntax_style = self.get_component_rich_style("command-name-syntax")
prefix = Text(f"{self.click_app_name} ", command_name_syntax_style)
new_value = self... | Update the preview box showing the command string to be executed | _update_execution_string_preview | python | Textualize/trogon | trogon/trogon.py | https://github.com/Textualize/trogon/blob/master/trogon/trogon.py | MIT |
def __init__(self, title: TextType, message: TextType) -> None:
"""Initialise the dialog.
Args:
title: The title for the dialog.
message: The message to show.
"""
super().__init__()
self._title = title
self._message = message | Initialise the dialog.
Args:
title: The title for the dialog.
message: The message to show.
| __init__ | python | Textualize/trogon | trogon/widgets/about.py | https://github.com/Textualize/trogon/blob/master/trogon/widgets/about.py | MIT |
def compose(self) -> ComposeResult:
"""Compose the content of the modal dialog."""
with Vertical():
with Center():
yield Static(self._title, classes="spaced")
yield Static(self._message, id="message", classes="spaced")
with Center(classes="spaced"):
... | Compose the content of the modal dialog. | compose | python | Textualize/trogon | trogon/widgets/about.py | https://github.com/Textualize/trogon/blob/master/trogon/widgets/about.py | MIT |
def _form_changed(self) -> None:
"""Take the current state of the form and build a UserCommandData from it,
then post a FormChanged message"""
command_schema = self.command_schema
path_from_root = command_schema.path_from_root
# Sentinel root value to make constructing the tree... | Take the current state of the form and build a UserCommandData from it,
then post a FormChanged message | _form_changed | python | Textualize/trogon | trogon/widgets/form.py | https://github.com/Textualize/trogon/blob/master/trogon/widgets/form.py | MIT |
def apply_filter(self, filter_query: str) -> bool:
"""Show or hide this ParameterControls depending on whether it matches the filter query or not.
Args:
filter_query: The string to filter on.
Returns:
True if the filter matched (and the widget is visible).
"""
... | Show or hide this ParameterControls depending on whether it matches the filter query or not.
Args:
filter_query: The string to filter on.
Returns:
True if the filter matched (and the widget is visible).
| apply_filter | python | Textualize/trogon | trogon/widgets/parameter_controls.py | https://github.com/Textualize/trogon/blob/master/trogon/widgets/parameter_controls.py | MIT |
def compose(self) -> ComposeResult:
"""Takes the schemas for each parameter of the current command, and converts it into a
form consisting of Textual widgets."""
schema = self.schema
name = schema.name
argument_type = schema.type
default = schema.default
help_text... | Takes the schemas for each parameter of the current command, and converts it into a
form consisting of Textual widgets. | compose | python | Textualize/trogon | trogon/widgets/parameter_controls.py | https://github.com/Textualize/trogon/blob/master/trogon/widgets/parameter_controls.py | MIT |
def make_widget_group(self) -> Iterable[ControlWidgetType]:
"""For this option, yield a single set of widgets required to receive user input for it."""
schema = self.schema
default = schema.default
parameter_type = schema.type
name = schema.name
multiple = schema.multiple... | For this option, yield a single set of widgets required to receive user input for it. | make_widget_group | python | Textualize/trogon | trogon/widgets/parameter_controls.py | https://github.com/Textualize/trogon/blob/master/trogon/widgets/parameter_controls.py | MIT |
def _apply_default_value(
control_widget: ControlWidgetType, default_value: Any
) -> None:
"""Set the default value of a parameter-handling widget."""
if isinstance(control_widget, Input):
control_widget.value = str(default_value)
control_widget.placeholder = f"{defau... | Set the default value of a parameter-handling widget. | _apply_default_value | python | Textualize/trogon | trogon/widgets/parameter_controls.py | https://github.com/Textualize/trogon/blob/master/trogon/widgets/parameter_controls.py | MIT |
def actions(self, state):
'actions are index where we can make a move'
actions = []
for index, char in enumerate(state):
if char == '_':
actions.append(index)
return actions | actions are index where we can make a move | actions | python | simpleai-team/simpleai | samples/machine_learning/tic_tac_toe.py | https://github.com/simpleai-team/simpleai/blob/master/samples/machine_learning/tic_tac_toe.py | MIT |
def find_location(rows, element_to_find):
'''Find the location of a piece in the puzzle.
Returns a tuple: row, column'''
for ir, row in enumerate(rows):
for ic, element in enumerate(row):
if element == element_to_find:
return ir, ic | Find the location of a piece in the puzzle.
Returns a tuple: row, column | find_location | python | simpleai-team/simpleai | samples/search/eight_puzzle.py | https://github.com/simpleai-team/simpleai/blob/master/samples/search/eight_puzzle.py | MIT |
def actions(self, state):
'''Returns a list of the pieces we can move to the empty space.'''
rows = string_to_list(state)
row_e, col_e = find_location(rows, 'e')
actions = []
if row_e > 0:
actions.append(rows[row_e - 1][col_e])
if row_e < 2:
actio... | Returns a list of the pieces we can move to the empty space. | actions | python | simpleai-team/simpleai | samples/search/eight_puzzle.py | https://github.com/simpleai-team/simpleai/blob/master/samples/search/eight_puzzle.py | MIT |
def result(self, state, action):
'''Return the resulting state after moving a piece to the empty space.
(the "action" parameter contains the piece to move)
'''
rows = string_to_list(state)
row_e, col_e = find_location(rows, 'e')
row_n, col_n = find_location(rows, actio... | Return the resulting state after moving a piece to the empty space.
(the "action" parameter contains the piece to move)
| result | python | simpleai-team/simpleai | samples/search/eight_puzzle.py | https://github.com/simpleai-team/simpleai/blob/master/samples/search/eight_puzzle.py | MIT |
def heuristic(self, state):
'''Returns an *estimation* of the distance from a state to the goal.
We are using the manhattan distance.
'''
rows = string_to_list(state)
distance = 0
for number in '12345678e':
row_n, col_n = find_location(rows, number)
... | Returns an *estimation* of the distance from a state to the goal.
We are using the manhattan distance.
| heuristic | python | simpleai-team/simpleai | samples/search/eight_puzzle.py | https://github.com/simpleai-team/simpleai/blob/master/samples/search/eight_puzzle.py | MIT |
def result(self, s, a):
'''Result of applying an action to a state.'''
# result: boat on opposite side, and numbers of missioners and
# cannibals updated according to the move
if s[2] == 0:
return (s[0] - a[1][0], s[1] - a[1][1], 1)
else:
return (s[0] + a[... | Result of applying an action to a state. | result | python | simpleai-team/simpleai | samples/search/missioners.py | https://github.com/simpleai-team/simpleai/blob/master/samples/search/missioners.py | MIT |
def mkconstraints():
"""
Make constraint list for binary constraint problem.
"""
constraints = []
for j in range(1, 10):
vars = ["%s%d" % (i, j) for i in uppercase[:9]]
constraints.extend((c, const_different) for c in combinations(vars, 2))
for i in uppercase[:9]:
vars ... |
Make constraint list for binary constraint problem.
| mkconstraints | python | simpleai-team/simpleai | samples/search/sudoku.py | https://github.com/simpleai-team/simpleai/blob/master/samples/search/sudoku.py | MIT |
def step(self, viewer=None):
"This method evolves one step in time"
if not self.is_completed(self.state):
for agent in self.agents:
action = agent.program(self.percept(agent, self.state))
next_state = self.do_action(self.state, action, agent)
i... | This method evolves one step in time | step | python | simpleai-team/simpleai | simpleai/environments.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/environments.py | MIT |
def learn(self, examples, attributes, parent_examples):
"""
A decision tree learner that *strictly* follows the pseudocode given in
AIMA. In 3rd edition, see Figure 18.5, page 702.
"""
if not examples:
return self.plurality_value(parent_examples)
elif len(set(... |
A decision tree learner that *strictly* follows the pseudocode given in
AIMA. In 3rd edition, see Figure 18.5, page 702.
| learn | python | simpleai-team/simpleai | simpleai/machine_learning/classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/classifiers.py | MIT |
def importance(self, attribute, examples):
"""
AIMA implies that importance should be information gain.
Since AIMA only defines it for binary features this implementation
was based on the wikipedia article:
http://en.wikipedia.org/wiki/Information_gain_in_decision_trees
"... |
AIMA implies that importance should be information gain.
Since AIMA only defines it for binary features this implementation
was based on the wikipedia article:
http://en.wikipedia.org/wiki/Information_gain_in_decision_trees
| importance | python | simpleai-team/simpleai | simpleai/machine_learning/classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/classifiers.py | MIT |
def save(self, filepath):
"""
Saves the classifier to `filepath`.
Because this classifier needs to save the dataset, it must
be something that can be pickled and not something like an
iterator.
"""
if not filepath or not isinstance(filepath, str):
rai... |
Saves the classifier to `filepath`.
Because this classifier needs to save the dataset, it must
be something that can be pickled and not something like an
iterator.
| save | python | simpleai-team/simpleai | simpleai/machine_learning/classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/classifiers.py | MIT |
def tree_to_str(root):
"""
Returns a string representation of a decision tree with
root node `root`.
"""
xs = []
for value, node, depth in iter_tree(root):
template = "{indent}"
if node is not root:
template += "case={value}\t"
if node.attribute is None:
... |
Returns a string representation of a decision tree with
root node `root`.
| tree_to_str | python | simpleai-team/simpleai | simpleai/machine_learning/classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/classifiers.py | MIT |
def take_branch(self, example):
"""
Returns a `DecisionTreeNode` instance that can better classify
`example` based on the selectors value.
If there are no more branches (ie, this node is a leaf) or the
attribute gives a value for an unexistent branch then this method
retu... |
Returns a `DecisionTreeNode` instance that can better classify
`example` based on the selectors value.
If there are no more branches (ie, this node is a leaf) or the
attribute gives a value for an unexistent branch then this method
returns None.
| take_branch | python | simpleai-team/simpleai | simpleai/machine_learning/classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/classifiers.py | MIT |
def _max_gain_split(self, examples):
"""
Returns an OnlineInformationGain of the attribute with
max gain based on `examples`.
"""
gains = self._new_set_of_gain_counters()
for example in examples:
for gain in gains:
gain.add(example)
win... |
Returns an OnlineInformationGain of the attribute with
max gain based on `examples`.
| _max_gain_split | python | simpleai-team/simpleai | simpleai/machine_learning/classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/classifiers.py | MIT |
def _new_set_of_gain_counters(self):
"""
Creates a new set of OnlineInformationGain objects
for each attribute.
"""
return [OnlineInformationGain(attribute, self.target)
for attribute in self.attributes] |
Creates a new set of OnlineInformationGain objects
for each attribute.
| _new_set_of_gain_counters | python | simpleai-team/simpleai | simpleai/machine_learning/classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/classifiers.py | MIT |
def precision(classifier, testset):
"""
Runs the classifier for each example in `testset`
and verifies that the classification is correct
using the `target`.
Returns a number between 0.0 and 1.0 with the
precision of classification for this test set.
"""
hit = 0
total = 0
for e... |
Runs the classifier for each example in `testset`
and verifies that the classification is correct
using the `target`.
Returns a number between 0.0 and 1.0 with the
precision of classification for this test set.
| precision | python | simpleai-team/simpleai | simpleai/machine_learning/evaluation.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/evaluation.py | MIT |
def kfold(dataset, problem, method, k=10):
"""
Does a k-fold on `dataset` with `method`.
This is, it randomly creates k-partitions of the dataset, and k-times
trains the method with k-1 parts and runs it with the partition left.
After all this, returns the overall success ratio.
"""
if k <=... |
Does a k-fold on `dataset` with `method`.
This is, it randomly creates k-partitions of the dataset, and k-times
trains the method with k-1 parts and runs it with the partition left.
After all this, returns the overall success ratio.
| kfold | python | simpleai-team/simpleai | simpleai/machine_learning/evaluation.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/evaluation.py | MIT |
def save(self, filepath):
"""
Pickles the tree and saves it into `filepath`
"""
if not filepath or not isinstance(filepath, str):
raise ValueError("Invalid filepath")
# Removes dataset so is not saved in the pickle
self.dataset = None
with open(filep... |
Pickles the tree and saves it into `filepath`
| save | python | simpleai-team/simpleai | simpleai/machine_learning/models.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/models.py | MIT |
def load(cls, filepath):
"""
Loads a pickled version of the classifier saved in `filepath`
"""
with open(filepath, "rb") as filehandler:
classifier = pickle.load(filehandler)
if not isinstance(classifier, Classifier):
raise ValueError("Pickled object is n... |
Loads a pickled version of the classifier saved in `filepath`
| load | python | simpleai-team/simpleai | simpleai/machine_learning/models.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/models.py | MIT |
def __init__(self, dataset, target_index):
"""
`dataset` should be an iterable, *not* an iterator.
`target_index` is the index in the vector where the classification
of an example is defined.
"""
super(VectorDataClassificationProblem, self).__init__()
try:
... |
`dataset` should be an iterable, *not* an iterator.
`target_index` is the index in the vector where the classification
of an example is defined.
| __init__ | python | simpleai-team/simpleai | simpleai/machine_learning/models.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/models.py | MIT |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.