body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
e9e2ad7ae55fbbdb6036cdb09258c201ed3945f13409777cb04f0582ecb49369 | @commands.command(hidden=True)
@commands.has_permissions(administrator=True)
async def create_table_duolingo_profile(self, ctx: commands.Context) -> None:
' Creates the DuolingoProfile table. '
member = ctx.author
(await ctx.message.delete())
if (await self.check_duolingo_profile_exists()):
retu... | Creates the DuolingoProfile table. | cogs/duolingo.py | create_table_duolingo_profile | yagomichalak/sloth-bot | 21 | python | @commands.command(hidden=True)
@commands.has_permissions(administrator=True)
async def create_table_duolingo_profile(self, ctx: commands.Context) -> None:
' '
member = ctx.author
(await ctx.message.delete())
if (await self.check_duolingo_profile_exists()):
return (await ctx.send(f'**Table `Duol... | @commands.command(hidden=True)
@commands.has_permissions(administrator=True)
async def create_table_duolingo_profile(self, ctx: commands.Context) -> None:
' '
member = ctx.author
(await ctx.message.delete())
if (await self.check_duolingo_profile_exists()):
return (await ctx.send(f'**Table `Duol... |
82d8f32888ef6bba57868ef5c618ba4622717a37e99051cb16b421efee39314a | @commands.command(hidden=True)
@commands.has_permissions(administrator=True)
async def drop_table_duolingo_profile(self, ctx: commands.Context) -> None:
' Creates the DuolingoProfile table. '
member = ctx.author
(await ctx.message.delete())
if (not (await self.check_duolingo_profile_exists())):
... | Creates the DuolingoProfile table. | cogs/duolingo.py | drop_table_duolingo_profile | yagomichalak/sloth-bot | 21 | python | @commands.command(hidden=True)
@commands.has_permissions(administrator=True)
async def drop_table_duolingo_profile(self, ctx: commands.Context) -> None:
' '
member = ctx.author
(await ctx.message.delete())
if (not (await self.check_duolingo_profile_exists())):
return (await ctx.send(f"**Table `... | @commands.command(hidden=True)
@commands.has_permissions(administrator=True)
async def drop_table_duolingo_profile(self, ctx: commands.Context) -> None:
' '
member = ctx.author
(await ctx.message.delete())
if (not (await self.check_duolingo_profile_exists())):
return (await ctx.send(f"**Table `... |
2770b9046f3434489015642eaef1d051fe8f61a8c9ac7fd6ce63a585ca34d66c | @commands.command(hidden=True)
@commands.has_permissions(administrator=True)
async def reset_table_duolingo_profile(self, ctx: commands.Context) -> None:
' Creates the DuolingoProfile table. '
member = ctx.author
(await ctx.message.delete())
if (not (await self.check_duolingo_profile_exists())):
... | Creates the DuolingoProfile table. | cogs/duolingo.py | reset_table_duolingo_profile | yagomichalak/sloth-bot | 21 | python | @commands.command(hidden=True)
@commands.has_permissions(administrator=True)
async def reset_table_duolingo_profile(self, ctx: commands.Context) -> None:
' '
member = ctx.author
(await ctx.message.delete())
if (not (await self.check_duolingo_profile_exists())):
return (await ctx.send(f"**Table ... | @commands.command(hidden=True)
@commands.has_permissions(administrator=True)
async def reset_table_duolingo_profile(self, ctx: commands.Context) -> None:
' '
member = ctx.author
(await ctx.message.delete())
if (not (await self.check_duolingo_profile_exists())):
return (await ctx.send(f"**Table ... |
b612c7a2b8487dbe7ef0bb768b2a7cd147b4ba67ba2d1986f6ceb0ff47d88209 | async def check_duolingo_profile_exists(self) -> bool:
' Checks whether the DuolingoProfile table exists. '
(mycursor, _) = (await the_database())
(await mycursor.execute("SHOW TABLE STATUS LIKE 'DuolingoProfile'"))
exists = (await mycursor.fetchone())
(await mycursor.close())
if exists:
... | Checks whether the DuolingoProfile table exists. | cogs/duolingo.py | check_duolingo_profile_exists | yagomichalak/sloth-bot | 21 | python | async def check_duolingo_profile_exists(self) -> bool:
' '
(mycursor, _) = (await the_database())
(await mycursor.execute("SHOW TABLE STATUS LIKE 'DuolingoProfile'"))
exists = (await mycursor.fetchone())
(await mycursor.close())
if exists:
return True
else:
return False | async def check_duolingo_profile_exists(self) -> bool:
' '
(mycursor, _) = (await the_database())
(await mycursor.execute("SHOW TABLE STATUS LIKE 'DuolingoProfile'"))
exists = (await mycursor.fetchone())
(await mycursor.close())
if exists:
return True
else:
return False<|doc... |
321b80b805f4b0b4d28d60d0a2290b5bbef43e6a8e3e7cd2ceff472b29c40ae7 | async def insert_duo_profile(self, user_id: int, duo_name: str) -> None:
" Inserts a Duolingo Profile.\n :param user_id: The ID of the user to insert.\n :param duo_name: The user's duolingo username. "
(mycursor, db) = (await the_database())
(await mycursor.execute('INSERT INTO DuolingoProfile... | Inserts a Duolingo Profile.
:param user_id: The ID of the user to insert.
:param duo_name: The user's duolingo username. | cogs/duolingo.py | insert_duo_profile | yagomichalak/sloth-bot | 21 | python | async def insert_duo_profile(self, user_id: int, duo_name: str) -> None:
" Inserts a Duolingo Profile.\n :param user_id: The ID of the user to insert.\n :param duo_name: The user's duolingo username. "
(mycursor, db) = (await the_database())
(await mycursor.execute('INSERT INTO DuolingoProfile... | async def insert_duo_profile(self, user_id: int, duo_name: str) -> None:
" Inserts a Duolingo Profile.\n :param user_id: The ID of the user to insert.\n :param duo_name: The user's duolingo username. "
(mycursor, db) = (await the_database())
(await mycursor.execute('INSERT INTO DuolingoProfile... |
5c0eaf8634ea73578b282353f79445d906868c8841cd3d837cc80cec70c17feb | async def get_duo_profile(self, user_id: int) -> List[Union[(int, str)]]:
' Gets a Duolingo Profile.\n :param user_id: The ID of the user to get. '
(mycursor, _) = (await the_database())
(await mycursor.execute('SELECT * FROM DuolingoProfile WHERE user_id = %s', (user_id,)))
duo_profile = (await ... | Gets a Duolingo Profile.
:param user_id: The ID of the user to get. | cogs/duolingo.py | get_duo_profile | yagomichalak/sloth-bot | 21 | python | async def get_duo_profile(self, user_id: int) -> List[Union[(int, str)]]:
' Gets a Duolingo Profile.\n :param user_id: The ID of the user to get. '
(mycursor, _) = (await the_database())
(await mycursor.execute('SELECT * FROM DuolingoProfile WHERE user_id = %s', (user_id,)))
duo_profile = (await ... | async def get_duo_profile(self, user_id: int) -> List[Union[(int, str)]]:
' Gets a Duolingo Profile.\n :param user_id: The ID of the user to get. '
(mycursor, _) = (await the_database())
(await mycursor.execute('SELECT * FROM DuolingoProfile WHERE user_id = %s', (user_id,)))
duo_profile = (await ... |
f4c8d6e0a11bc5e1c4e7cd3b3bbb7332209624733b35bf1162865cbb929411ea | async def update_duo_profile(self, user_id: int, duo_name: str) -> None:
" Updates a Duolingo Profile.\n :param user_id: The ID of the user to update.\n :param duo_name: The user's new duolingo username. "
(mycursor, db) = (await the_database())
(await mycursor.execute('UPDATE DuolingoProfile ... | Updates a Duolingo Profile.
:param user_id: The ID of the user to update.
:param duo_name: The user's new duolingo username. | cogs/duolingo.py | update_duo_profile | yagomichalak/sloth-bot | 21 | python | async def update_duo_profile(self, user_id: int, duo_name: str) -> None:
" Updates a Duolingo Profile.\n :param user_id: The ID of the user to update.\n :param duo_name: The user's new duolingo username. "
(mycursor, db) = (await the_database())
(await mycursor.execute('UPDATE DuolingoProfile ... | async def update_duo_profile(self, user_id: int, duo_name: str) -> None:
" Updates a Duolingo Profile.\n :param user_id: The ID of the user to update.\n :param duo_name: The user's new duolingo username. "
(mycursor, db) = (await the_database())
(await mycursor.execute('UPDATE DuolingoProfile ... |
cf6b725549ed65faf644c60a152962397c3d5adce11fa3d95814e93ec53ddc02 | async def delete_duo_profile(self, user_id: int) -> None:
' Deletes a Duolingo Profile.\n :param user_id: The ID of the user to delete. '
(mycursor, db) = (await the_database())
(await mycursor.execute('DELETE FROM DuolingoProfile WHERE user_id = %s', (user_id,)))
(await db.commit())
(await m... | Deletes a Duolingo Profile.
:param user_id: The ID of the user to delete. | cogs/duolingo.py | delete_duo_profile | yagomichalak/sloth-bot | 21 | python | async def delete_duo_profile(self, user_id: int) -> None:
' Deletes a Duolingo Profile.\n :param user_id: The ID of the user to delete. '
(mycursor, db) = (await the_database())
(await mycursor.execute('DELETE FROM DuolingoProfile WHERE user_id = %s', (user_id,)))
(await db.commit())
(await m... | async def delete_duo_profile(self, user_id: int) -> None:
' Deletes a Duolingo Profile.\n :param user_id: The ID of the user to delete. '
(mycursor, db) = (await the_database())
(await mycursor.execute('DELETE FROM DuolingoProfile WHERE user_id = %s', (user_id,)))
(await db.commit())
(await m... |
344dbe0ae9b072fe415ecc5b7696ef2935d105593ac9444bf22492a5e8b45299 | def score_funct(x):
'\n The score function for Iris anneal.\n @param x:\n @return:\n '
global best_score
global input_data
global output_data
network.copy_memory(x)
actual_output = []
for input_data in training_input:
output_data = network.compute_regression(input_data)
... | The score function for Iris anneal.
@param x:
@return: | vol2/vol2-python-examples/examples/example_aco_iris.py | score_funct | AbleLynn/AIalgorithm | 777 | python | def score_funct(x):
'\n The score function for Iris anneal.\n @param x:\n @return:\n '
global best_score
global input_data
global output_data
network.copy_memory(x)
actual_output = []
for input_data in training_input:
output_data = network.compute_regression(input_data)
... | def score_funct(x):
'\n The score function for Iris anneal.\n @param x:\n @return:\n '
global best_score
global input_data
global output_data
network.copy_memory(x)
actual_output = []
for input_data in training_input:
output_data = network.compute_regression(input_data)
... |
d6a33f3553506164d9265a98f6ca4bff886cc27f1a568ac9347e650862227cc0 | def make_human_survey_class(group):
'Creates a form class for a group of questions\n\n The top-level attributes of the generated class correspond to the question_ids from\n amgut.lib.human_survey_supp structures\n\n Select fields are generated for questions that require a single response, and sets\n of ... | Creates a form class for a group of questions
The top-level attributes of the generated class correspond to the question_ids from
amgut.lib.human_survey_supp structures
Select fields are generated for questions that require a single response, and sets
of checkboxes for questions that can have multiple responses | amgut/handlers/human_survey.py | make_human_survey_class | mortonjt/american-gut-web | 0 | python | def make_human_survey_class(group):
'Creates a form class for a group of questions\n\n The top-level attributes of the generated class correspond to the question_ids from\n amgut.lib.human_survey_supp structures\n\n Select fields are generated for questions that require a single response, and sets\n of ... | def make_human_survey_class(group):
'Creates a form class for a group of questions\n\n The top-level attributes of the generated class correspond to the question_ids from\n amgut.lib.human_survey_supp structures\n\n Select fields are generated for questions that require a single response, and sets\n of ... |
ac03054b1307c7ac52b3a8d2d222a80de34d52189324b264b62516c5f069657e | def ignore(self, irc, msg, args):
"requires no arguments\n\n Does nothing. Useful sometimes for sequencing commands when you don't\n care about their non-error return values.\n "
if irc.nested:
msg.tag('ignored')
irc.reply('') | requires no arguments
Does nothing. Useful sometimes for sequencing commands when you don't
care about their non-error return values. | plugins/Utilities/plugin.py | ignore | Supybot/Supybot | 40 | python | def ignore(self, irc, msg, args):
"requires no arguments\n\n Does nothing. Useful sometimes for sequencing commands when you don't\n care about their non-error return values.\n "
if irc.nested:
msg.tag('ignored')
irc.reply() | def ignore(self, irc, msg, args):
"requires no arguments\n\n Does nothing. Useful sometimes for sequencing commands when you don't\n care about their non-error return values.\n "
if irc.nested:
msg.tag('ignored')
irc.reply()<|docstring|>requires no arguments
Does nothing. ... |
c66ccfa423afe34aba88d25a8985b427624e2e61f780ff1ce02c8d66b9fcb35a | def success(self, irc, msg, args, text):
"[<text>]\n\n Does nothing except to reply with a success message. This is useful\n when you want to run multiple commands as nested commands, and don't\n care about their output as long as they're successful. An error, of\n course, will break o... | [<text>]
Does nothing except to reply with a success message. This is useful
when you want to run multiple commands as nested commands, and don't
care about their output as long as they're successful. An error, of
course, will break out of this command. <text>, if given, will be
appended to the end of the success m... | plugins/Utilities/plugin.py | success | Supybot/Supybot | 40 | python | def success(self, irc, msg, args, text):
"[<text>]\n\n Does nothing except to reply with a success message. This is useful\n when you want to run multiple commands as nested commands, and don't\n care about their output as long as they're successful. An error, of\n course, will break o... | def success(self, irc, msg, args, text):
"[<text>]\n\n Does nothing except to reply with a success message. This is useful\n when you want to run multiple commands as nested commands, and don't\n care about their output as long as they're successful. An error, of\n course, will break o... |
0a0989425d402c4c27f2a4694f541a6d8f50c26490a5f7fd55405e1f9d4461fa | def last(self, irc, msg, args):
"<text> [<text> ...]\n\n Returns the last argument given. Useful when you'd like multiple\n nested commands to run, but only the output of the last one to be\n returned.\n "
args = filter(None, args)
if args:
irc.reply(args[(- 1)])
els... | <text> [<text> ...]
Returns the last argument given. Useful when you'd like multiple
nested commands to run, but only the output of the last one to be
returned. | plugins/Utilities/plugin.py | last | Supybot/Supybot | 40 | python | def last(self, irc, msg, args):
"<text> [<text> ...]\n\n Returns the last argument given. Useful when you'd like multiple\n nested commands to run, but only the output of the last one to be\n returned.\n "
args = filter(None, args)
if args:
irc.reply(args[(- 1)])
els... | def last(self, irc, msg, args):
"<text> [<text> ...]\n\n Returns the last argument given. Useful when you'd like multiple\n nested commands to run, but only the output of the last one to be\n returned.\n "
args = filter(None, args)
if args:
irc.reply(args[(- 1)])
els... |
747d8f0ea97eb33eb461472ca3afc3f3c12ad8fc753b53bb751fd948529bbf97 | def echo(self, irc, msg, args, text):
'<text>\n\n Returns the arguments given it. Uses our standard substitute on the\n string(s) given to it; $nick (or $who), $randomNick, $randomInt,\n $botnick, $channel, $user, $host, $today, $now, and $randomDate are all\n handled appropriately.\n ... | <text>
Returns the arguments given it. Uses our standard substitute on the
string(s) given to it; $nick (or $who), $randomNick, $randomInt,
$botnick, $channel, $user, $host, $today, $now, and $randomDate are all
handled appropriately. | plugins/Utilities/plugin.py | echo | Supybot/Supybot | 40 | python | def echo(self, irc, msg, args, text):
'<text>\n\n Returns the arguments given it. Uses our standard substitute on the\n string(s) given to it; $nick (or $who), $randomNick, $randomInt,\n $botnick, $channel, $user, $host, $today, $now, and $randomDate are all\n handled appropriately.\n ... | def echo(self, irc, msg, args, text):
'<text>\n\n Returns the arguments given it. Uses our standard substitute on the\n string(s) given to it; $nick (or $who), $randomNick, $randomInt,\n $botnick, $channel, $user, $host, $today, $now, and $randomDate are all\n handled appropriately.\n ... |
355704f24b39ddbcac73581f321eb60d0d83aa65068f0571da7d3be952de2316 | def shuffle(self, irc, msg, args, things):
'<arg> [<arg> ...]\n\n Shuffles the arguments given it.\n '
random.shuffle(things)
irc.reply(' '.join(things)) | <arg> [<arg> ...]
Shuffles the arguments given it. | plugins/Utilities/plugin.py | shuffle | Supybot/Supybot | 40 | python | def shuffle(self, irc, msg, args, things):
'<arg> [<arg> ...]\n\n Shuffles the arguments given it.\n '
random.shuffle(things)
irc.reply(' '.join(things)) | def shuffle(self, irc, msg, args, things):
'<arg> [<arg> ...]\n\n Shuffles the arguments given it.\n '
random.shuffle(things)
irc.reply(' '.join(things))<|docstring|><arg> [<arg> ...]
Shuffles the arguments given it.<|endoftext|> |
d785d6555d4ee4ca7ef45ffdedcc41777d4875ba4083087880dbb68bf91fb669 | def apply(self, irc, msg, args, command, rest):
'<command> <text>\n\n Tokenizes <text> and calls <command> with the resulting arguments.\n '
args = [((token and token) or '""') for token in rest]
text = ' '.join(args)
commands = command.split()
commands = map(callbacks.canonicalName, c... | <command> <text>
Tokenizes <text> and calls <command> with the resulting arguments. | plugins/Utilities/plugin.py | apply | Supybot/Supybot | 40 | python | def apply(self, irc, msg, args, command, rest):
'<command> <text>\n\n Tokenizes <text> and calls <command> with the resulting arguments.\n '
args = [((token and token) or '') for token in rest]
text = ' '.join(args)
commands = command.split()
commands = map(callbacks.canonicalName, com... | def apply(self, irc, msg, args, command, rest):
'<command> <text>\n\n Tokenizes <text> and calls <command> with the resulting arguments.\n '
args = [((token and token) or '') for token in rest]
text = ' '.join(args)
commands = command.split()
commands = map(callbacks.canonicalName, com... |
bf4fa41fd18b153c09833bfb0732dc68787d451f89844f08edc99fe0648720c8 | def __init__(self, ontology_name: str='base', is_debug: bool=False):
"\n Created:\n 26-Mar-2019\n example@example.com\n * based on 'find-entity'\n Updated:\n 15-Jul-2019\n example@example.com\n * add 'find' method\n Updated:\... | Created:
26-Mar-2019
example@example.com
* based on 'find-entity'
Updated:
15-Jul-2019
example@example.com
* add 'find' method
Updated:
13-Dec-2019
example@example.com
* load dictionaries by ontology name
https://github.ibm.com/GTS-CDO/unstructured-analytics/issues/1582 | python/datadict/core/svc/find_patterns.py | __init__ | jiportilla/ontology | 0 | python | def __init__(self, ontology_name: str='base', is_debug: bool=False):
"\n Created:\n 26-Mar-2019\n example@example.com\n * based on 'find-entity'\n Updated:\n 15-Jul-2019\n example@example.com\n * add 'find' method\n Updated:\... | def __init__(self, ontology_name: str='base', is_debug: bool=False):
"\n Created:\n 26-Mar-2019\n example@example.com\n * based on 'find-entity'\n Updated:\n 15-Jul-2019\n example@example.com\n * add 'find' method\n Updated:\... |
de3fe529676c34c0ed0e2c62608c871a0973440b10a310636ccfb7178e84d60b | def long_distance(self) -> ValuesView[list]:
'\n sample input:\n { \'Aix 5.2 Workload\': [ \'aix+5.2+workload\',\n \'aix_5.2_workload\' ],\n \'Aix 5.2 Workload Partitions\': [ \'aix+5.2+workload+partitions\',\n ... | sample input:
{ 'Aix 5.2 Workload': [ 'aix+5.2+workload',
'aix_5.2_workload' ],
'Aix 5.2 Workload Partitions': [ 'aix+5.2+workload+partitions',
'aix_5.2_workload_partitions' ],
...
}
sample output:
[ { "p... | python/datadict/core/svc/find_patterns.py | long_distance | jiportilla/ontology | 0 | python | def long_distance(self) -> ValuesView[list]:
'\n sample input:\n { \'Aix 5.2 Workload\': [ \'aix+5.2+workload\',\n \'aix_5.2_workload\' ],\n \'Aix 5.2 Workload Partitions\': [ \'aix+5.2+workload+partitions\',\n ... | def long_distance(self) -> ValuesView[list]:
'\n sample input:\n { \'Aix 5.2 Workload\': [ \'aix+5.2+workload\',\n \'aix_5.2_workload\' ],\n \'Aix 5.2 Workload Partitions\': [ \'aix+5.2+workload+partitions\',\n ... |
5849e39f4d4939f862b83d7118e04f55b9cce1d20435e798c2add6271e0d722b | def add_special_word_to_embedding_vectors(embedding_vectors, word_map, word):
'\n Args:\n embedding_vectors: np.ndarray\n shape: (n_vocab, n_dimension)\n the embedding vectors, always generated by gensim.model.wv.vectors \n '
if (word_map.get(word) is not None):
ret... | Args:
embedding_vectors: np.ndarray
shape: (n_vocab, n_dimension)
the embedding vectors, always generated by gensim.model.wv.vectors | tharsis/nn/embedding.py | add_special_word_to_embedding_vectors | neutronest/ml_research_python | 0 | python | def add_special_word_to_embedding_vectors(embedding_vectors, word_map, word):
'\n Args:\n embedding_vectors: np.ndarray\n shape: (n_vocab, n_dimension)\n the embedding vectors, always generated by gensim.model.wv.vectors \n '
if (word_map.get(word) is not None):
ret... | def add_special_word_to_embedding_vectors(embedding_vectors, word_map, word):
'\n Args:\n embedding_vectors: np.ndarray\n shape: (n_vocab, n_dimension)\n the embedding vectors, always generated by gensim.model.wv.vectors \n '
if (word_map.get(word) is not None):
ret... |
d4a67a8af210d60d12fb008c024c35963deca1e034b70041a5d00904abb865cb | @click.command(help='Measure runtime of a command. Return statistical data.')
@click.option('-s', '--batch-size', default=10, help='How many tests should be run per batch')
@click.option('-b', '--batches', default=1000, help='How many batches of tests to run')
@click.option('--suppress-stdout/--no-suppress-stdout', def... | Run a command given on the command line.
Arguments are parsed from the following list. | ajattara/ajattara.py | run_program | fennekki/ajattara | 0 | python | @click.command(help='Measure runtime of a command. Return statistical data.')
@click.option('-s', '--batch-size', default=10, help='How many tests should be run per batch')
@click.option('-b', '--batches', default=1000, help='How many batches of tests to run')
@click.option('--suppress-stdout/--no-suppress-stdout', def... | @click.command(help='Measure runtime of a command. Return statistical data.')
@click.option('-s', '--batch-size', default=10, help='How many tests should be run per batch')
@click.option('-b', '--batches', default=1000, help='How many batches of tests to run')
@click.option('--suppress-stdout/--no-suppress-stdout', def... |
76931ff7def4afbede024610fa5498db0d1cf5d578b1e5a28132f6065cde873b | def __init__(self, batch_count, batch_size, function, args=[], kwargs={}):
'Initialise an object.\n\n Params:\n function (function): The function to be run and measured\n batch_count (int): The amount of batches to run\n batch_size (int): The number of runs per batch\n ... | Initialise an object.
Params:
function (function): The function to be run and measured
batch_count (int): The amount of batches to run
batch_size (int): The number of runs per batch | ajattara/ajattara.py | __init__ | fennekki/ajattara | 0 | python | def __init__(self, batch_count, batch_size, function, args=[], kwargs={}):
'Initialise an object.\n\n Params:\n function (function): The function to be run and measured\n batch_count (int): The amount of batches to run\n batch_size (int): The number of runs per batch\n ... | def __init__(self, batch_count, batch_size, function, args=[], kwargs={}):
'Initialise an object.\n\n Params:\n function (function): The function to be run and measured\n batch_count (int): The amount of batches to run\n batch_size (int): The number of runs per batch\n ... |
fe39be336b17c826b8f910554e3cf3a82643e9c70304c1a55ba4edf7fac8035a | def run(self):
'Run the function a specified amount of times.'
returns = []
for i in range(self.__batch_count):
start = time()
for _ in range(self.__batch_size):
self.__function(*self.__args, **self.__kwargs)
stop = time()
returns.append((stop - start))
return... | Run the function a specified amount of times. | ajattara/ajattara.py | run | fennekki/ajattara | 0 | python | def run(self):
returns = []
for i in range(self.__batch_count):
start = time()
for _ in range(self.__batch_size):
self.__function(*self.__args, **self.__kwargs)
stop = time()
returns.append((stop - start))
return returns | def run(self):
returns = []
for i in range(self.__batch_count):
start = time()
for _ in range(self.__batch_size):
self.__function(*self.__args, **self.__kwargs)
stop = time()
returns.append((stop - start))
return returns<|docstring|>Run the function a specifi... |
c5c4ea74ff3ff8e6beb4fe90858ca10b5874b47da7f9ef71bf5fa80ab73f6ae5 | def sel_observations(self, idx):
'Select a subset of the observations in idata_orig.\n\n **Not implemented**: This method must be implemented by the SamplingWrapper subclasses.\n It is documented here to show its format and call signature.\n\n Parameters\n ----------\n idx\n ... | Select a subset of the observations in idata_orig.
**Not implemented**: This method must be implemented by the SamplingWrapper subclasses.
It is documented here to show its format and call signature.
Parameters
----------
idx
Indexes to separate from the rest of the observed data.
Returns
-------
modified_observ... | arviz/wrappers/base.py | sel_observations | neha-shah99/arviz | 1 | python | def sel_observations(self, idx):
'Select a subset of the observations in idata_orig.\n\n **Not implemented**: This method must be implemented by the SamplingWrapper subclasses.\n It is documented here to show its format and call signature.\n\n Parameters\n ----------\n idx\n ... | def sel_observations(self, idx):
'Select a subset of the observations in idata_orig.\n\n **Not implemented**: This method must be implemented by the SamplingWrapper subclasses.\n It is documented here to show its format and call signature.\n\n Parameters\n ----------\n idx\n ... |
5fa73bacf00418c78de0bbd4163cff4d8a44ba503ea406c1aa798ebc5b3c3fe1 | def sample(self, modified_observed_data):
'Sample ``self.model`` on the ``modified_observed_data`` subset.\n\n **Not implemented**: This method must be implemented by the SamplingWrapper subclasses.\n It is documented here to show its format and call signature.\n\n Parameters\n ---------... | Sample ``self.model`` on the ``modified_observed_data`` subset.
**Not implemented**: This method must be implemented by the SamplingWrapper subclasses.
It is documented here to show its format and call signature.
Parameters
----------
modified_observed_data
Data to fit the model on.
Returns
-------
fitted_model
... | arviz/wrappers/base.py | sample | neha-shah99/arviz | 1 | python | def sample(self, modified_observed_data):
'Sample ``self.model`` on the ``modified_observed_data`` subset.\n\n **Not implemented**: This method must be implemented by the SamplingWrapper subclasses.\n It is documented here to show its format and call signature.\n\n Parameters\n ---------... | def sample(self, modified_observed_data):
'Sample ``self.model`` on the ``modified_observed_data`` subset.\n\n **Not implemented**: This method must be implemented by the SamplingWrapper subclasses.\n It is documented here to show its format and call signature.\n\n Parameters\n ---------... |
1c44b899cc82108943ec1041c3144b2d6db7d6d4c5588c545d1ed7de49f1ca45 | def get_inference_data(self, fitted_model):
'Convert the ``fitted_model`` to an InferenceData object.\n\n **Not implemented**: This method must be implemented by the SamplingWrapper subclasses.\n It is documented here to show its format and call signature.\n\n Parameters\n ----------\n ... | Convert the ``fitted_model`` to an InferenceData object.
**Not implemented**: This method must be implemented by the SamplingWrapper subclasses.
It is documented here to show its format and call signature.
Parameters
----------
fitted_model
Result of the current fit.
Returns
-------
idata_current: InferenceData
... | arviz/wrappers/base.py | get_inference_data | neha-shah99/arviz | 1 | python | def get_inference_data(self, fitted_model):
'Convert the ``fitted_model`` to an InferenceData object.\n\n **Not implemented**: This method must be implemented by the SamplingWrapper subclasses.\n It is documented here to show its format and call signature.\n\n Parameters\n ----------\n ... | def get_inference_data(self, fitted_model):
'Convert the ``fitted_model`` to an InferenceData object.\n\n **Not implemented**: This method must be implemented by the SamplingWrapper subclasses.\n It is documented here to show its format and call signature.\n\n Parameters\n ----------\n ... |
595863f04fa7d3c6614c3c62c79bbc0afd9c58547f533320e1e1a69b6b2fcc4b | def point_log_likelihood(self, observation, parameters):
'Pointwise log likelihood function.\n\n Parameters\n ----------\n observation\n Pointwise observation on which to calculate the log likelihood\n parameters\n Parameters on which the log likelihood is condition... | Pointwise log likelihood function.
Parameters
----------
observation
Pointwise observation on which to calculate the log likelihood
parameters
Parameters on which the log likelihood is conditioned.
Returns
-------
point_log_likelihood: float
Value of the log likelihood of ``observation`` given ``parameter... | arviz/wrappers/base.py | point_log_likelihood | neha-shah99/arviz | 1 | python | def point_log_likelihood(self, observation, parameters):
'Pointwise log likelihood function.\n\n Parameters\n ----------\n observation\n Pointwise observation on which to calculate the log likelihood\n parameters\n Parameters on which the log likelihood is condition... | def point_log_likelihood(self, observation, parameters):
'Pointwise log likelihood function.\n\n Parameters\n ----------\n observation\n Pointwise observation on which to calculate the log likelihood\n parameters\n Parameters on which the log likelihood is condition... |
5d4063318b96110508d581eafbf5dd24d126570fd5df8b5fbe99d558279b21d3 | def log_likelihood__i(self, excluded_obs, idata__i):
'Get the log likelilhood samples :math:`\\log p_{post(-i)}(y_i)`.\n\n Calculate the log likelihood of the data contained in excluded_obs using the\n model fitted with this data excluded, the results of which are stored in ``idata__i``.\n\n Pa... | Get the log likelilhood samples :math:`\log p_{post(-i)}(y_i)`.
Calculate the log likelihood of the data contained in excluded_obs using the
model fitted with this data excluded, the results of which are stored in ``idata__i``.
Parameters
----------
excluded_obs
Observations for which to calculate their log likel... | arviz/wrappers/base.py | log_likelihood__i | neha-shah99/arviz | 1 | python | def log_likelihood__i(self, excluded_obs, idata__i):
'Get the log likelilhood samples :math:`\\log p_{post(-i)}(y_i)`.\n\n Calculate the log likelihood of the data contained in excluded_obs using the\n model fitted with this data excluded, the results of which are stored in ``idata__i``.\n\n Pa... | def log_likelihood__i(self, excluded_obs, idata__i):
'Get the log likelilhood samples :math:`\\log p_{post(-i)}(y_i)`.\n\n Calculate the log likelihood of the data contained in excluded_obs using the\n model fitted with this data excluded, the results of which are stored in ``idata__i``.\n\n Pa... |
1b04cca5fafefe8ec5165df962998bee086530aa323edadc9a0c82266eaaf7d8 | def _check_method_is_implemented(self, method, *args):
'Check a given method is implemented.'
try:
getattr(self, method)(*args)
except NotImplementedError:
return False
except:
return True
return True | Check a given method is implemented. | arviz/wrappers/base.py | _check_method_is_implemented | neha-shah99/arviz | 1 | python | def _check_method_is_implemented(self, method, *args):
try:
getattr(self, method)(*args)
except NotImplementedError:
return False
except:
return True
return True | def _check_method_is_implemented(self, method, *args):
try:
getattr(self, method)(*args)
except NotImplementedError:
return False
except:
return True
return True<|docstring|>Check a given method is implemented.<|endoftext|> |
0e5876eb54ed0e1abd3766a12f42055c3068b6d1ae8d37772f40473d17ecf014 | def check_implemented_methods(self, methods):
'Check that all methods listed are implemented.\n\n Not all functions that require refitting need to have all the methods implemented in\n order to work properly. This function shoulg be used before using the SamplingWrapper and\n its subclasses to ... | Check that all methods listed are implemented.
Not all functions that require refitting need to have all the methods implemented in
order to work properly. This function shoulg be used before using the SamplingWrapper and
its subclasses to get informative error messages.
Parameters
----------
methods: list
Check ... | arviz/wrappers/base.py | check_implemented_methods | neha-shah99/arviz | 1 | python | def check_implemented_methods(self, methods):
'Check that all methods listed are implemented.\n\n Not all functions that require refitting need to have all the methods implemented in\n order to work properly. This function shoulg be used before using the SamplingWrapper and\n its subclasses to ... | def check_implemented_methods(self, methods):
'Check that all methods listed are implemented.\n\n Not all functions that require refitting need to have all the methods implemented in\n order to work properly. This function shoulg be used before using the SamplingWrapper and\n its subclasses to ... |
28a0aecfc3c1f9966b3bab742d9b16388ed8fb45e8c27adf768162abe049fd14 | def AddAnkiTubeButton() -> None:
'\n Adds a button to the add card dialogue that opens AnkiTube\n\n Callback function for add_cards_did_init hook\n '
at_button = aqt.qt.QPushButton()
at_button.show() | Adds a button to the add card dialogue that opens AnkiTube
Callback function for add_cards_did_init hook | __init__.py | AddAnkiTubeButton | hunt0x3r/youtube2gif | 1 | python | def AddAnkiTubeButton() -> None:
'\n Adds a button to the add card dialogue that opens AnkiTube\n\n Callback function for add_cards_did_init hook\n '
at_button = aqt.qt.QPushButton()
at_button.show() | def AddAnkiTubeButton() -> None:
'\n Adds a button to the add card dialogue that opens AnkiTube\n\n Callback function for add_cards_did_init hook\n '
at_button = aqt.qt.QPushButton()
at_button.show()<|docstring|>Adds a button to the add card dialogue that opens AnkiTube
Callback function for add_c... |
5f50cde4a33ae1306dce2c729eedae01a7953a022fcb7cda4122f39c3b19ffe8 | def evaluate_posenet(self, tag='real', valset='s911'):
'\n evaluate the performance of posenet on 3 kinds of dataset\n check every clip performance.\n '
start_time = time()
with torch.no_grad():
self.model_pos.load_state_dict(self.model_pos_train.state_dict())
self.model... | evaluate the performance of posenet on 3 kinds of dataset
check every clip performance. | estimator/posegan_evaluate.py | evaluate_posenet | Garfield-kh/PoseTriplet | 9 | python | def evaluate_posenet(self, tag='real', valset='s911'):
'\n evaluate the performance of posenet on 3 kinds of dataset\n check every clip performance.\n '
start_time = time()
with torch.no_grad():
self.model_pos.load_state_dict(self.model_pos_train.state_dict())
self.model... | def evaluate_posenet(self, tag='real', valset='s911'):
'\n evaluate the performance of posenet on 3 kinds of dataset\n check every clip performance.\n '
start_time = time()
with torch.no_grad():
self.model_pos.load_state_dict(self.model_pos_train.state_dict())
self.model... |
2bf63a784397516a3c05720207bde10e2d940aed97a1b6542e3f5fd615980c98 | def evaluate_trajnet(self, tag='real', valset='s911'):
'\n evaluate the performance of posenet on 3 kinds of dataset\n '
start_time = time()
with torch.no_grad():
self.model_traj.load_state_dict(self.model_traj_train.state_dict())
self.model_traj.eval()
epoch_p1_3d_vali... | evaluate the performance of posenet on 3 kinds of dataset | estimator/posegan_evaluate.py | evaluate_trajnet | Garfield-kh/PoseTriplet | 9 | python | def evaluate_trajnet(self, tag='real', valset='s911'):
'\n \n '
start_time = time()
with torch.no_grad():
self.model_traj.load_state_dict(self.model_traj_train.state_dict())
self.model_traj.eval()
epoch_p1_3d_valid = 0
N_valid = 0
self.summary.test_iter_... | def evaluate_trajnet(self, tag='real', valset='s911'):
'\n \n '
start_time = time()
with torch.no_grad():
self.model_traj.load_state_dict(self.model_traj_train.state_dict())
self.model_traj.eval()
epoch_p1_3d_valid = 0
N_valid = 0
self.summary.test_iter_... |
c94327ed9bae080c7eba93f883983da6b6cf9d436459826ea854469499272647 | def vis_result(self, tag='real', valset='s911'):
'\n evaluate the performance of posenet on 3 kinds of dataset\n check every clip performance.\n '
start_time = time()
with torch.no_grad():
self.model_pos.load_state_dict(self.model_pos_train.state_dict())
self.model_pos.e... | evaluate the performance of posenet on 3 kinds of dataset
check every clip performance. | estimator/posegan_evaluate.py | vis_result | Garfield-kh/PoseTriplet | 9 | python | def vis_result(self, tag='real', valset='s911'):
'\n evaluate the performance of posenet on 3 kinds of dataset\n check every clip performance.\n '
start_time = time()
with torch.no_grad():
self.model_pos.load_state_dict(self.model_pos_train.state_dict())
self.model_pos.e... | def vis_result(self, tag='real', valset='s911'):
'\n evaluate the performance of posenet on 3 kinds of dataset\n check every clip performance.\n '
start_time = time()
with torch.no_grad():
self.model_pos.load_state_dict(self.model_pos_train.state_dict())
self.model_pos.e... |
bd57d777a9e0c97ae87f3177f049a3a5b4eb6a72bd9ad3b8cc4926ec7df2adc1 | def save_result(self, valset):
'\n evaluate and save the s15678 / s15678_flip\n '
start_time = time()
result_all_lst = []
with torch.no_grad():
self.model_pos.load_state_dict(self.model_pos_train.state_dict())
self.model_pos.eval()
self.model_traj.load_state_dict(se... | evaluate and save the s15678 / s15678_flip | estimator/posegan_evaluate.py | save_result | Garfield-kh/PoseTriplet | 9 | python | def save_result(self, valset):
'\n \n '
start_time = time()
result_all_lst = []
with torch.no_grad():
self.model_pos.load_state_dict(self.model_pos_train.state_dict())
self.model_pos.eval()
self.model_traj.load_state_dict(self.model_traj_train.state_dict())
... | def save_result(self, valset):
'\n \n '
start_time = time()
result_all_lst = []
with torch.no_grad():
self.model_pos.load_state_dict(self.model_pos_train.state_dict())
self.model_pos.eval()
self.model_traj.load_state_dict(self.model_traj_train.state_dict())
... |
0bc4dc91b4f7737e74962c97f00d33221052e6ed72b527cd8ce8545a7b3ec586 | def write_standard_bvh(self, bvhfileName, prediction3dpoint):
'\n :param outbvhfilepath:\n :param prediction3dpoint:\n :return:\n '
for frame in prediction3dpoint:
for point3d in frame:
point3d[0] *= 100
point3d[1] *= 100
point3d[2] *= 100
... | :param outbvhfilepath:
:param prediction3dpoint:
:return: | estimator/posegan_evaluate.py | write_standard_bvh | Garfield-kh/PoseTriplet | 9 | python | def write_standard_bvh(self, bvhfileName, prediction3dpoint):
'\n :param outbvhfilepath:\n :param prediction3dpoint:\n :return:\n '
for frame in prediction3dpoint:
for point3d in frame:
point3d[0] *= 100
point3d[1] *= 100
point3d[2] *= 100
... | def write_standard_bvh(self, bvhfileName, prediction3dpoint):
'\n :param outbvhfilepath:\n :param prediction3dpoint:\n :return:\n '
for frame in prediction3dpoint:
for point3d in frame:
point3d[0] *= 100
point3d[1] *= 100
point3d[2] *= 100
... |
06800d6c2745e2835bb3d1562b2076b3e6aadd4d994960f469e14fe622529205 | def smooth_l1_loss(y_true: Tensor, y_pred: Tensor, beta: float=1.0) -> Tensor:
"Calculate Smooth L1 Loss between two tensors.\n\n This method can be used with TensorFlow tensors:\n ```python\n\n true = tf.constant([[0,1,0,0], [0,0,0,1], [0,0,1,0], [1,0,0,0]])\n pred = tf.constant([[0.1,0.9,0.05,0.05], [... | Calculate Smooth L1 Loss between two tensors.
This method can be used with TensorFlow tensors:
```python
true = tf.constant([[0,1,0,0], [0,0,0,1], [0,0,1,0], [1,0,0,0]])
pred = tf.constant([[0.1,0.9,0.05,0.05], [0.1,0.2,0.0,0.7], [0.0,0.15,0.8,0.05], [1.0,0.0,0.0,0.0]])
Smooth_L1 = fe.backend.smooth_l1_loss(y_pred=pr... | fastestimator/backend/_smooth_l1_loss.py | smooth_l1_loss | aashokvardhan/fastestimator | 0 | python | def smooth_l1_loss(y_true: Tensor, y_pred: Tensor, beta: float=1.0) -> Tensor:
"Calculate Smooth L1 Loss between two tensors.\n\n This method can be used with TensorFlow tensors:\n ```python\n\n true = tf.constant([[0,1,0,0], [0,0,0,1], [0,0,1,0], [1,0,0,0]])\n pred = tf.constant([[0.1,0.9,0.05,0.05], [... | def smooth_l1_loss(y_true: Tensor, y_pred: Tensor, beta: float=1.0) -> Tensor:
"Calculate Smooth L1 Loss between two tensors.\n\n This method can be used with TensorFlow tensors:\n ```python\n\n true = tf.constant([[0,1,0,0], [0,0,0,1], [0,0,1,0], [1,0,0,0]])\n pred = tf.constant([[0.1,0.9,0.05,0.05], [... |
0c13d721a9a70dfdd995e4a2239a0e39237d161e85131db28f747a4e58544306 | def __init__(self, action_space_dim=3, hidden_dim=256) -> None:
'Initialization of the DQN\n\n Args:\n action_space_dim (int, optional): dimension of the action space. Defaults to 3.\n hidden_dim (int, optional): dimension of the embedding space of the input image. Defaults to 256.\n ... | Initialization of the DQN
Args:
action_space_dim (int, optional): dimension of the action space. Defaults to 3.
hidden_dim (int, optional): dimension of the embedding space of the input image. Defaults to 256. | agent.py | __init__ | MattiaMolon/Atari-Pong-RL | 2 | python | def __init__(self, action_space_dim=3, hidden_dim=256) -> None:
'Initialization of the DQN\n\n Args:\n action_space_dim (int, optional): dimension of the action space. Defaults to 3.\n hidden_dim (int, optional): dimension of the embedding space of the input image. Defaults to 256.\n ... | def __init__(self, action_space_dim=3, hidden_dim=256) -> None:
'Initialization of the DQN\n\n Args:\n action_space_dim (int, optional): dimension of the action space. Defaults to 3.\n hidden_dim (int, optional): dimension of the embedding space of the input image. Defaults to 256.\n ... |
6885449d06ae864f3268f3910deb295ebcf10a25e5a6fcc267ebf6f598facd83 | def forward(self, x: Tensor) -> Tensor:
'Forward an image throught the network\n\n Args:\n x (Tensor): input image to feed forward into the network\n\n Returns:\n Tensor: The action predicted\n '
x = F.relu(self.cnv1(x))
x = F.relu(self.cnv2(x))
x = self.flat1(... | Forward an image throught the network
Args:
x (Tensor): input image to feed forward into the network
Returns:
Tensor: The action predicted | agent.py | forward | MattiaMolon/Atari-Pong-RL | 2 | python | def forward(self, x: Tensor) -> Tensor:
'Forward an image throught the network\n\n Args:\n x (Tensor): input image to feed forward into the network\n\n Returns:\n Tensor: The action predicted\n '
x = F.relu(self.cnv1(x))
x = F.relu(self.cnv2(x))
x = self.flat1(... | def forward(self, x: Tensor) -> Tensor:
'Forward an image throught the network\n\n Args:\n x (Tensor): input image to feed forward into the network\n\n Returns:\n Tensor: The action predicted\n '
x = F.relu(self.cnv1(x))
x = F.relu(self.cnv2(x))
x = self.flat1(... |
87e35f1e661d44d73298e12b62025fcad691b52b171f6eb817ab3bbbea5679f4 | def __init__(self, player_id: int=1, name: str='Ugo', batch_size: int=128, gamma: float=0.98, memory_size: int=40000) -> None:
'Initialization for the DQN agent\n\n Args:\n player_id (int, optional): Side of the board on which to play. Defaults to 1.\n name (str, optional): Name of the ... | Initialization for the DQN agent
Args:
player_id (int, optional): Side of the board on which to play. Defaults to 1.
name (str, optional): Name of the player. Defaults to "Ugo".
batch_size (int, optional): Batch size of the update. Defaults to 128.
gamma (float, optional): Gamme value for update decay.... | agent.py | __init__ | MattiaMolon/Atari-Pong-RL | 2 | python | def __init__(self, player_id: int=1, name: str='Ugo', batch_size: int=128, gamma: float=0.98, memory_size: int=40000) -> None:
'Initialization for the DQN agent\n\n Args:\n player_id (int, optional): Side of the board on which to play. Defaults to 1.\n name (str, optional): Name of the ... | def __init__(self, player_id: int=1, name: str='Ugo', batch_size: int=128, gamma: float=0.98, memory_size: int=40000) -> None:
'Initialization for the DQN agent\n\n Args:\n player_id (int, optional): Side of the board on which to play. Defaults to 1.\n name (str, optional): Name of the ... |
e853ca31bfea271f51bef6bd17a241c6ea597f1fe257722c8fed01c7530e6079 | def update_policy_net(self) -> None:
'Update policy_net via Q-learning approximation'
if (len(self.memory) < self.batch_size):
return
transitions = self.memory.sample(self.batch_size)
batch = Transition(*zip(*transitions))
non_final_mask = (1 - torch.tensor(batch.done, dtype=torch.uint8).to(... | Update policy_net via Q-learning approximation | agent.py | update_policy_net | MattiaMolon/Atari-Pong-RL | 2 | python | def update_policy_net(self) -> None:
if (len(self.memory) < self.batch_size):
return
transitions = self.memory.sample(self.batch_size)
batch = Transition(*zip(*transitions))
non_final_mask = (1 - torch.tensor(batch.done, dtype=torch.uint8).to(torch.device(device)))
non_final_mask = non_... | def update_policy_net(self) -> None:
if (len(self.memory) < self.batch_size):
return
transitions = self.memory.sample(self.batch_size)
batch = Transition(*zip(*transitions))
non_final_mask = (1 - torch.tensor(batch.done, dtype=torch.uint8).to(torch.device(device)))
non_final_mask = non_... |
fa66e11847a523c9d594f5fe3fa532c321ef6a83132d9a96da29e83b41e9bc27 | def update_target_net(self) -> None:
'Update target net'
self.target_net.load_state_dict(self.policy_net.state_dict()) | Update target net | agent.py | update_target_net | MattiaMolon/Atari-Pong-RL | 2 | python | def update_target_net(self) -> None:
self.target_net.load_state_dict(self.policy_net.state_dict()) | def update_target_net(self) -> None:
self.target_net.load_state_dict(self.policy_net.state_dict())<|docstring|>Update target net<|endoftext|> |
fb95a5f05a40ccbbca56274816cb47e58ba4a3aabc4e329916f0557a8a172619 | def get_action(self, ob: np.ndarray, epsilon: float=0.1, train: bool=False) -> int:
'Interface function that returns the action that the agent took based\n on the observation ob\n\n Args:\n ob (np.ndarray, optional): Current observation from the game.\n epsilon (float, optional):... | Interface function that returns the action that the agent took based
on the observation ob
Args:
ob (np.ndarray, optional): Current observation from the game.
epsilon (float, optional): Epsilon for epsilon greedy. Defaults to 0.1.
train (bool, optional): Identifies if the agent is in testing or training ph... | agent.py | get_action | MattiaMolon/Atari-Pong-RL | 2 | python | def get_action(self, ob: np.ndarray, epsilon: float=0.1, train: bool=False) -> int:
'Interface function that returns the action that the agent took based\n on the observation ob\n\n Args:\n ob (np.ndarray, optional): Current observation from the game.\n epsilon (float, optional):... | def get_action(self, ob: np.ndarray, epsilon: float=0.1, train: bool=False) -> int:
'Interface function that returns the action that the agent took based\n on the observation ob\n\n Args:\n ob (np.ndarray, optional): Current observation from the game.\n epsilon (float, optional):... |
d0948b42468a60a9ab3b48b51e6f7a093102a371ef4afb59a51015bd6353252a | def get_name(self) -> str:
'Return name of the agent\n\n Returns:\n str: name of the agent\n '
return self.name | Return name of the agent
Returns:
str: name of the agent | agent.py | get_name | MattiaMolon/Atari-Pong-RL | 2 | python | def get_name(self) -> str:
'Return name of the agent\n\n Returns:\n str: name of the agent\n '
return self.name | def get_name(self) -> str:
'Return name of the agent\n\n Returns:\n str: name of the agent\n '
return self.name<|docstring|>Return name of the agent
Returns:
str: name of the agent<|endoftext|> |
617158c8883ac70100d224ce04c8350090793d62cb980832f7dd3a3eed98d3d6 | def reset(self) -> None:
'Clean the buffers of the memory'
self.memory.test_buffer = []
self.memory.train_buffer = [] | Clean the buffers of the memory | agent.py | reset | MattiaMolon/Atari-Pong-RL | 2 | python | def reset(self) -> None:
self.memory.test_buffer = []
self.memory.train_buffer = [] | def reset(self) -> None:
self.memory.test_buffer = []
self.memory.train_buffer = []<|docstring|>Clean the buffers of the memory<|endoftext|> |
3f944d6f8f015ccb4ae80b9dd747d432f59ee7ae76df1922a0f7a899b07c5368 | def load_model(self, path_ai: str='weights/hibrid_tuned_best.ai', path_optm: str=None) -> None:
'Load model weights and optimizer from a certain path\n\n Args:\n path_ai (str, optional): Path to model weights. Defaults to "weights/hibrid_tuned_best.ai".\n path_optm (str, optional): Path... | Load model weights and optimizer from a certain path
Args:
path_ai (str, optional): Path to model weights. Defaults to "weights/hibrid_tuned_best.ai".
path_optm (str, optional): Path to optimizer weights. Defaults to None. | agent.py | load_model | MattiaMolon/Atari-Pong-RL | 2 | python | def load_model(self, path_ai: str='weights/hibrid_tuned_best.ai', path_optm: str=None) -> None:
'Load model weights and optimizer from a certain path\n\n Args:\n path_ai (str, optional): Path to model weights. Defaults to "weights/hibrid_tuned_best.ai".\n path_optm (str, optional): Path... | def load_model(self, path_ai: str='weights/hibrid_tuned_best.ai', path_optm: str=None) -> None:
'Load model weights and optimizer from a certain path\n\n Args:\n path_ai (str, optional): Path to model weights. Defaults to "weights/hibrid_tuned_best.ai".\n path_optm (str, optional): Path... |
34566ecedca4ad1eae5e0653376f2d858c97551d6afad90fb00ecec24e4a3fc1 | def save_model(self, dir: str, ep: int) -> None:
'Save model to file\n\n Args:\n dir (str): Directory to where save the model\n ep (int): episode number\n '
torch.save(self.policy_net.state_dict(), (dir + f'/DQN_{(ep + 1)}.ai'))
torch.save(self.optimizer.state_dict(), (di... | Save model to file
Args:
dir (str): Directory to where save the model
ep (int): episode number | agent.py | save_model | MattiaMolon/Atari-Pong-RL | 2 | python | def save_model(self, dir: str, ep: int) -> None:
'Save model to file\n\n Args:\n dir (str): Directory to where save the model\n ep (int): episode number\n '
torch.save(self.policy_net.state_dict(), (dir + f'/DQN_{(ep + 1)}.ai'))
torch.save(self.optimizer.state_dict(), (di... | def save_model(self, dir: str, ep: int) -> None:
'Save model to file\n\n Args:\n dir (str): Directory to where save the model\n ep (int): episode number\n '
torch.save(self.policy_net.state_dict(), (dir + f'/DQN_{(ep + 1)}.ai'))
torch.save(self.optimizer.state_dict(), (di... |
ac76be0b2834601361aa1594fbadf360514ac9cfc178316dfad7d3ccce82f182 | def push_to_train_buffer(self, ob: np.ndarray, action: int, reward: int, next_ob: np.ndarray, done: bool) -> None:
'Push a transition to the memory train buffer\n\n Args:\n ob (np.ndarray): Obsertation/state at time t\n action (int): Action at time t\n reward (int): Reward fo... | Push a transition to the memory train buffer
Args:
ob (np.ndarray): Obsertation/state at time t
action (int): Action at time t
reward (int): Reward for taking action a in state s at time t
next_ob (np.ndarray): Observation/state at time t+1
done (bool): Defines if the game is finished or not | agent.py | push_to_train_buffer | MattiaMolon/Atari-Pong-RL | 2 | python | def push_to_train_buffer(self, ob: np.ndarray, action: int, reward: int, next_ob: np.ndarray, done: bool) -> None:
'Push a transition to the memory train buffer\n\n Args:\n ob (np.ndarray): Obsertation/state at time t\n action (int): Action at time t\n reward (int): Reward fo... | def push_to_train_buffer(self, ob: np.ndarray, action: int, reward: int, next_ob: np.ndarray, done: bool) -> None:
'Push a transition to the memory train buffer\n\n Args:\n ob (np.ndarray): Obsertation/state at time t\n action (int): Action at time t\n reward (int): Reward fo... |
cffc3e7346086c67be913b5decd8b5e49f8d6b77a7619bd429982e6657c31570 | def push_to_test_buffer(self, ob: np.ndarray) -> None:
'Push a transition to the train buffer\n\n Args:\n ob (np.ndarray): Observation to push to the buffer\n '
ob = self.preprocess_ob(ob)
self.memory.push_to_test_buffer(ob)
if (len(self.memory.test_buffer) == self.memory.test_b... | Push a transition to the train buffer
Args:
ob (np.ndarray): Observation to push to the buffer | agent.py | push_to_test_buffer | MattiaMolon/Atari-Pong-RL | 2 | python | def push_to_test_buffer(self, ob: np.ndarray) -> None:
'Push a transition to the train buffer\n\n Args:\n ob (np.ndarray): Observation to push to the buffer\n '
ob = self.preprocess_ob(ob)
self.memory.push_to_test_buffer(ob)
if (len(self.memory.test_buffer) == self.memory.test_b... | def push_to_test_buffer(self, ob: np.ndarray) -> None:
'Push a transition to the train buffer\n\n Args:\n ob (np.ndarray): Observation to push to the buffer\n '
ob = self.preprocess_ob(ob)
self.memory.push_to_test_buffer(ob)
if (len(self.memory.test_buffer) == self.memory.test_b... |
078be610bca7de2543a8baeac970e44cfd6215c557603219b1b8841fd66b03e1 | def get_stack_from_train_buffer(self, ob: np.ndarray) -> Tensor:
'Get stack of preprocessed observations/states from train buffer\n\n Args:\n ob (np.ndarray): Current observation/state\n\n Returns:\n Tensor: Stack of preprocessed observations/states\n '
ob = self.prepr... | Get stack of preprocessed observations/states from train buffer
Args:
ob (np.ndarray): Current observation/state
Returns:
Tensor: Stack of preprocessed observations/states | agent.py | get_stack_from_train_buffer | MattiaMolon/Atari-Pong-RL | 2 | python | def get_stack_from_train_buffer(self, ob: np.ndarray) -> Tensor:
'Get stack of preprocessed observations/states from train buffer\n\n Args:\n ob (np.ndarray): Current observation/state\n\n Returns:\n Tensor: Stack of preprocessed observations/states\n '
ob = self.prepr... | def get_stack_from_train_buffer(self, ob: np.ndarray) -> Tensor:
'Get stack of preprocessed observations/states from train buffer\n\n Args:\n ob (np.ndarray): Current observation/state\n\n Returns:\n Tensor: Stack of preprocessed observations/states\n '
ob = self.prepr... |
927bb8b33fbfd6655ac26bc6bab2a7badbad2f2d68c1aba69c7086180b31d581 | def get_stack_from_test_buffer(self, ob: np.ndarray) -> Tensor:
'Get stack of preprocessed observations/states from test buffer\n\n Args:\n ob (np.ndarray): Current observation/state\n\n Returns:\n Tensor: Stack of preprocessed observations/states\n '
ob = self.preproc... | Get stack of preprocessed observations/states from test buffer
Args:
ob (np.ndarray): Current observation/state
Returns:
Tensor: Stack of preprocessed observations/states | agent.py | get_stack_from_test_buffer | MattiaMolon/Atari-Pong-RL | 2 | python | def get_stack_from_test_buffer(self, ob: np.ndarray) -> Tensor:
'Get stack of preprocessed observations/states from test buffer\n\n Args:\n ob (np.ndarray): Current observation/state\n\n Returns:\n Tensor: Stack of preprocessed observations/states\n '
ob = self.preproc... | def get_stack_from_test_buffer(self, ob: np.ndarray) -> Tensor:
'Get stack of preprocessed observations/states from test buffer\n\n Args:\n ob (np.ndarray): Current observation/state\n\n Returns:\n Tensor: Stack of preprocessed observations/states\n '
ob = self.preproc... |
d4718b5952099c832ea3e1bd4446cd0b53211d9e6b8b14167ea45f85b524f878 | def preprocess_ob(self, ob: np.ndarray) -> Tensor:
'Preprocess observation:\n\n - shrink the image to 100x100\n\n - transform it to black and white\n\n - transform it into a Tensor\n\n\n Args:\n ob (np.ndarray): Observation to preprocess\n\n Returns:\n Tensor... | Preprocess observation:
- shrink the image to 100x100
- transform it to black and white
- transform it into a Tensor
Args:
ob (np.ndarray): Observation to preprocess
Returns:
Tensor: Preprocessed observation | agent.py | preprocess_ob | MattiaMolon/Atari-Pong-RL | 2 | python | def preprocess_ob(self, ob: np.ndarray) -> Tensor:
'Preprocess observation:\n\n - shrink the image to 100x100\n\n - transform it to black and white\n\n - transform it into a Tensor\n\n\n Args:\n ob (np.ndarray): Observation to preprocess\n\n Returns:\n Tensor... | def preprocess_ob(self, ob: np.ndarray) -> Tensor:
'Preprocess observation:\n\n - shrink the image to 100x100\n\n - transform it to black and white\n\n - transform it into a Tensor\n\n\n Args:\n ob (np.ndarray): Observation to preprocess\n\n Returns:\n Tensor... |
f99b7309450c749c3ef5cfbfb691e7f3e12941c146e4509d7883541a4bbc1b50 | def main():
"\n Initiates the Brand Sample which makes multiple requests against\n the Business Communications API. The requests this sample\n makes are:\n - Create a brand\n - Gets the brand details\n - Updates the created brand's display name\n - Lists all brands available\n ... | Initiates the Brand Sample which makes multiple requests against
the Business Communications API. The requests this sample
makes are:
- Create a brand
- Gets the brand details
- Updates the created brand's display name
- Lists all brands available
- Delete the created brand | brand_sample.py | main | google-business-communications/bc-bm-python-command-line-examples | 1 | python | def main():
"\n Initiates the Brand Sample which makes multiple requests against\n the Business Communications API. The requests this sample\n makes are:\n - Create a brand\n - Gets the brand details\n - Updates the created brand's display name\n - Lists all brands available\n ... | def main():
"\n Initiates the Brand Sample which makes multiple requests against\n the Business Communications API. The requests this sample\n makes are:\n - Create a brand\n - Gets the brand details\n - Updates the created brand's display name\n - Lists all brands available\n ... |
d6758f0378768fb8efca92b79518d368aabf77d570f73edea246f6d4254b89a5 | def create_brand():
"\n Creates a brand with the name 'Test Brand'.\n\n Returns:\n brand (Brand): The brand object that was created.\n "
brand = brands_service.Create(Brand(displayName='Test Brand'))
print(brand)
return brand | Creates a brand with the name 'Test Brand'.
Returns:
brand (Brand): The brand object that was created. | brand_sample.py | create_brand | google-business-communications/bc-bm-python-command-line-examples | 1 | python | def create_brand():
"\n Creates a brand with the name 'Test Brand'.\n\n Returns:\n brand (Brand): The brand object that was created.\n "
brand = brands_service.Create(Brand(displayName='Test Brand'))
print(brand)
return brand | def create_brand():
"\n Creates a brand with the name 'Test Brand'.\n\n Returns:\n brand (Brand): The brand object that was created.\n "
brand = brands_service.Create(Brand(displayName='Test Brand'))
print(brand)
return brand<|docstring|>Creates a brand with the name 'Test Brand'.
Retur... |
c231363f70ffdcdbb7b56fad8c8bd8698747018e4e1e966d283d1de3a9d79511 | def update_brand(brand, display_name):
'\n Updates the passed in brand object with a new display name.\n\n Args:\n brand (Brand): The brand to be updated.\n display_name (str): The new display name.\n\n Returns:\n updated_brand (Brand): The updated brand object.\n '
brand.displa... | Updates the passed in brand object with a new display name.
Args:
brand (Brand): The brand to be updated.
display_name (str): The new display name.
Returns:
updated_brand (Brand): The updated brand object. | brand_sample.py | update_brand | google-business-communications/bc-bm-python-command-line-examples | 1 | python | def update_brand(brand, display_name):
'\n Updates the passed in brand object with a new display name.\n\n Args:\n brand (Brand): The brand to be updated.\n display_name (str): The new display name.\n\n Returns:\n updated_brand (Brand): The updated brand object.\n '
brand.displa... | def update_brand(brand, display_name):
'\n Updates the passed in brand object with a new display name.\n\n Args:\n brand (Brand): The brand to be updated.\n display_name (str): The new display name.\n\n Returns:\n updated_brand (Brand): The updated brand object.\n '
brand.displa... |
3f1afef010398bde7cfde06ef7a9e4c9fa8a8730c816e30cd8f876f396f5d8ab | def get_brand(brand_name):
"\n Based on the brand name, looks up the brand details.\n\n Args:\n brand_name (str): The unique identifier for the brand in\n 'brands/BRAND_ID' format.\n\n Returns:\n brand (Brand): The matching brand object.\n "
brand = brands_service.Get(Businessco... | Based on the brand name, looks up the brand details.
Args:
brand_name (str): The unique identifier for the brand in
'brands/BRAND_ID' format.
Returns:
brand (Brand): The matching brand object. | brand_sample.py | get_brand | google-business-communications/bc-bm-python-command-line-examples | 1 | python | def get_brand(brand_name):
"\n Based on the brand name, looks up the brand details.\n\n Args:\n brand_name (str): The unique identifier for the brand in\n 'brands/BRAND_ID' format.\n\n Returns:\n brand (Brand): The matching brand object.\n "
brand = brands_service.Get(Businessco... | def get_brand(brand_name):
"\n Based on the brand name, looks up the brand details.\n\n Args:\n brand_name (str): The unique identifier for the brand in\n 'brands/BRAND_ID' format.\n\n Returns:\n brand (Brand): The matching brand object.\n "
brand = brands_service.Get(Businessco... |
de7e7d53c8d479a2be4fb022861f6914ea6f0d3766db8518856276e40911e5ad | def list_brands():
'\n Lists all brands for the configured Cloud project.\n\n Returns:\n brands (Brand[]): The list of brands for the configured Cloud project.\n '
brands = brands_service.List(BusinesscommunicationsBrandsListRequest())
print(brands)
return brands | Lists all brands for the configured Cloud project.
Returns:
brands (Brand[]): The list of brands for the configured Cloud project. | brand_sample.py | list_brands | google-business-communications/bc-bm-python-command-line-examples | 1 | python | def list_brands():
'\n Lists all brands for the configured Cloud project.\n\n Returns:\n brands (Brand[]): The list of brands for the configured Cloud project.\n '
brands = brands_service.List(BusinesscommunicationsBrandsListRequest())
print(brands)
return brands | def list_brands():
'\n Lists all brands for the configured Cloud project.\n\n Returns:\n brands (Brand[]): The list of brands for the configured Cloud project.\n '
brands = brands_service.List(BusinesscommunicationsBrandsListRequest())
print(brands)
return brands<|docstring|>Lists all br... |
1eb9566aa9ccfd77cb32367d4266d50a2b35a56fec56d5126e66b1c4b63fd32d | def delete_brand(brand_name):
"\n Based on the brand name, deletes the brand. Deleting a brand with\n associated agents will also result in the agents also being deleted.\n Only brands without verified agents can be deleted.\n\n Args:\n brand_name (str): The unique identifier for the brand in\n ... | Based on the brand name, deletes the brand. Deleting a brand with
associated agents will also result in the agents also being deleted.
Only brands without verified agents can be deleted.
Args:
brand_name (str): The unique identifier for the brand in
'brands/BRAND_ID' format. | brand_sample.py | delete_brand | google-business-communications/bc-bm-python-command-line-examples | 1 | python | def delete_brand(brand_name):
"\n Based on the brand name, deletes the brand. Deleting a brand with\n associated agents will also result in the agents also being deleted.\n Only brands without verified agents can be deleted.\n\n Args:\n brand_name (str): The unique identifier for the brand in\n ... | def delete_brand(brand_name):
"\n Based on the brand name, deletes the brand. Deleting a brand with\n associated agents will also result in the agents also being deleted.\n Only brands without verified agents can be deleted.\n\n Args:\n brand_name (str): The unique identifier for the brand in\n ... |
a3b44f298b5f887b00d81478871730a608aaaf3a106755ed24659501b3fea6ff | @njit(nogil=True, parallel=True, cache=__cache)
def fixity2d_to_dofs1d(fixity2d: np.ndarray, inds: np.ndarray=None):
"\n Returns the indices of the degrees of freedoms\n being supressed. \n\n Optionally, global indices of the rows in 'fixity2d' \n array can be provided by the optional argument 'inds'.\n... | Returns the indices of the degrees of freedoms
being supressed.
Optionally, global indices of the rows in 'fixity2d'
array can be provided by the optional argument 'inds'.
Parameters
----------
fixity2d : np.ndarray(bool)[:, :]
2d numpy array of booleans. It has as many rows as nodes, and as
many columns a... | src/dewloosh/solid/fem/utils.py | fixity2d_to_dofs1d | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def fixity2d_to_dofs1d(fixity2d: np.ndarray, inds: np.ndarray=None):
"\n Returns the indices of the degrees of freedoms\n being supressed. \n\n Optionally, global indices of the rows in 'fixity2d' \n array can be provided by the optional argument 'inds'.\n... | @njit(nogil=True, parallel=True, cache=__cache)
def fixity2d_to_dofs1d(fixity2d: np.ndarray, inds: np.ndarray=None):
"\n Returns the indices of the degrees of freedoms\n being supressed. \n\n Optionally, global indices of the rows in 'fixity2d' \n array can be provided by the optional argument 'inds'.\n... |
4af9cf8ca6ee601c2198679c0a37d418bc1f740d45015dd258c7d65ab339c322 | @njit(nogil=True, parallel=True, cache=__cache)
def nodes2d_to_dofs1d(inds: np.ndarray, values: np.ndarray):
"\n Returns a tuple of degree of freedom indices and data, \n based on a nodal definition.\n\n Parameters\n ----------\n inds : np.ndarray\n 1d numpy array of integers, listing global n... | Returns a tuple of degree of freedom indices and data,
based on a nodal definition.
Parameters
----------
inds : np.ndarray
1d numpy array of integers, listing global node indices.
values : int of shape (nN, nDOF, nRHS)
3d numpy array of floats, listing values for each node
in 'inds'.
Returns
-------
do... | src/dewloosh/solid/fem/utils.py | nodes2d_to_dofs1d | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def nodes2d_to_dofs1d(inds: np.ndarray, values: np.ndarray):
"\n Returns a tuple of degree of freedom indices and data, \n based on a nodal definition.\n\n Parameters\n ----------\n inds : np.ndarray\n 1d numpy array of integers, listing global n... | @njit(nogil=True, parallel=True, cache=__cache)
def nodes2d_to_dofs1d(inds: np.ndarray, values: np.ndarray):
"\n Returns a tuple of degree of freedom indices and data, \n based on a nodal definition.\n\n Parameters\n ----------\n inds : np.ndarray\n 1d numpy array of integers, listing global n... |
0e5d0d42c1d26bdc0d24b53c61053825302779061153e6f088be81112f3c4ee2 | @njit(nogil=True, cache=__cache, parallel=True)
def weighted_stiffness_bulk(K: np.ndarray, weights: np.ndarray):
'\n Returns a weighted stiffness matrix.\n\n Parameters\n ----------\n K : np.ndarray\n 2d numpy array of floats\n\n weights : np.ndarray\n 1d numpy array of floats\n\n Re... | Returns a weighted stiffness matrix.
Parameters
----------
K : np.ndarray
2d numpy array of floats
weights : np.ndarray
1d numpy array of floats
Returns
-------
Kw : np.ndarray
2d numpy array of floats
Notes
-----
(1) It is assumed that the first axis of K runs
along the elements. | src/dewloosh/solid/fem/utils.py | weighted_stiffness_bulk | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, cache=__cache, parallel=True)
def weighted_stiffness_bulk(K: np.ndarray, weights: np.ndarray):
'\n Returns a weighted stiffness matrix.\n\n Parameters\n ----------\n K : np.ndarray\n 2d numpy array of floats\n\n weights : np.ndarray\n 1d numpy array of floats\n\n Re... | @njit(nogil=True, cache=__cache, parallel=True)
def weighted_stiffness_bulk(K: np.ndarray, weights: np.ndarray):
'\n Returns a weighted stiffness matrix.\n\n Parameters\n ----------\n K : np.ndarray\n 2d numpy array of floats\n\n weights : np.ndarray\n 1d numpy array of floats\n\n Re... |
7024ed9761c0e40a0c78172196cc4c575500e08c3cbcfcb6bfa1fe2d969295b4 | @njit(nogil=True, parallel=True, cache=__cache)
def irows_icols_bulk(edofs: np.ndarray):
'\n Returns row and column index data for several finite elements.\n\n Parameters\n ----------\n edofs : np.ndarray\n 2d numpy array. Each row has the meaning of global degree of \n freedom numbering f... | Returns row and column index data for several finite elements.
Parameters
----------
edofs : np.ndarray
2d numpy array. Each row has the meaning of global degree of
freedom numbering for a given finite element.
Returns
-------
irows, icols : np.ndarray, np.ndarray
Global indices of the rows and columns o... | src/dewloosh/solid/fem/utils.py | irows_icols_bulk | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def irows_icols_bulk(edofs: np.ndarray):
'\n Returns row and column index data for several finite elements.\n\n Parameters\n ----------\n edofs : np.ndarray\n 2d numpy array. Each row has the meaning of global degree of \n freedom numbering f... | @njit(nogil=True, parallel=True, cache=__cache)
def irows_icols_bulk(edofs: np.ndarray):
'\n Returns row and column index data for several finite elements.\n\n Parameters\n ----------\n edofs : np.ndarray\n 2d numpy array. Each row has the meaning of global degree of \n freedom numbering f... |
2ac0a68316a0c116a0de45f26e23d6bb43f803b24486dcb6944fadb1b635f764 | @njit(nogil=True, cache=__cache, parallel=True)
def irows_icols_bulk_filtered(edofs: np.ndarray, inds: np.ndarray):
'\n Returns row and column index data for finite elements specified\n by the index array `inds`.\n\n Parameters\n ----------\n edofs : np.ndarray\n 2d numpy array. Each row has t... | Returns row and column index data for finite elements specified
by the index array `inds`.
Parameters
----------
edofs : np.ndarray
2d numpy array. Each row has the meaning of global degree of
freedom numbering for a given finite element.
inds: np.ndarray
1d numpy array of integers specifying active elem... | src/dewloosh/solid/fem/utils.py | irows_icols_bulk_filtered | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, cache=__cache, parallel=True)
def irows_icols_bulk_filtered(edofs: np.ndarray, inds: np.ndarray):
'\n Returns row and column index data for finite elements specified\n by the index array `inds`.\n\n Parameters\n ----------\n edofs : np.ndarray\n 2d numpy array. Each row has t... | @njit(nogil=True, cache=__cache, parallel=True)
def irows_icols_bulk_filtered(edofs: np.ndarray, inds: np.ndarray):
'\n Returns row and column index data for finite elements specified\n by the index array `inds`.\n\n Parameters\n ----------\n edofs : np.ndarray\n 2d numpy array. Each row has t... |
a73ef4a72fe620bb8db786b8f9212a409c50033457612ea81a1a9b76df953723 | @njit(nogil=True, cache=__cache, parallel=True)
def topo_to_gnum(topo: np.ndarray, ndofn: int):
'\n Returns global dof numbering based on element \n topology data.\n\n Parameters\n ----------\n topo : np.ndarray\n 2d numpy array of integers. Topology array listing global\n node numbers ... | Returns global dof numbering based on element
topology data.
Parameters
----------
topo : np.ndarray
2d numpy array of integers. Topology array listing global
node numbers for several elements.
ndofn : int
Number of degrees of freedoms per node.
Returns
-------
gnum : np.ndarray
2d numpy array of in... | src/dewloosh/solid/fem/utils.py | topo_to_gnum | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, cache=__cache, parallel=True)
def topo_to_gnum(topo: np.ndarray, ndofn: int):
'\n Returns global dof numbering based on element \n topology data.\n\n Parameters\n ----------\n topo : np.ndarray\n 2d numpy array of integers. Topology array listing global\n node numbers ... | @njit(nogil=True, cache=__cache, parallel=True)
def topo_to_gnum(topo: np.ndarray, ndofn: int):
'\n Returns global dof numbering based on element \n topology data.\n\n Parameters\n ----------\n topo : np.ndarray\n 2d numpy array of integers. Topology array listing global\n node numbers ... |
e1dba835e64e1e581a477c5bdb5fb8ead2a5f32b825fedbf30c2049846e75028 | @njit(nogil=True, parallel=True, fastmath=True, cache=__cache)
def assemble_load_vector(values: ndarray, gnum: ndarray, N: int=(- 1)):
"\n Returns global dof numbering based on element \n topology data.\n\n Parameters\n ----------\n values : np.ndarray of shape (nE, nEVAB, nRHS)\n 3d numpy arr... | Returns global dof numbering based on element
topology data.
Parameters
----------
values : np.ndarray of shape (nE, nEVAB, nRHS)
3d numpy array of floats, representing element data.
The length of the second axis matches the the number of
degrees of freedom per cell.
gnum : int
Global indices of loca... | src/dewloosh/solid/fem/utils.py | assemble_load_vector | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, fastmath=True, cache=__cache)
def assemble_load_vector(values: ndarray, gnum: ndarray, N: int=(- 1)):
"\n Returns global dof numbering based on element \n topology data.\n\n Parameters\n ----------\n values : np.ndarray of shape (nE, nEVAB, nRHS)\n 3d numpy arr... | @njit(nogil=True, parallel=True, fastmath=True, cache=__cache)
def assemble_load_vector(values: ndarray, gnum: ndarray, N: int=(- 1)):
"\n Returns global dof numbering based on element \n topology data.\n\n Parameters\n ----------\n values : np.ndarray of shape (nE, nEVAB, nRHS)\n 3d numpy arr... |
bb5fb8e547bdfb0ca18dea5cda2cc68d56f59a3b8eb845531a5a88c83671b05a | @njit(nogil=True, parallel=True, cache=__cache)
def approximation_matrix(ndf: ndarray, NDOFN: int):
'Returns a matrix of approximation coefficients \n for all elements.'
(nE, nNE) = ndf.shape[:2]
N = (nNE * NDOFN)
nappr = np.eye(N, dtype=ndf.dtype)
res = np.zeros((nE, N, N), dtype=ndf.dtype)
... | Returns a matrix of approximation coefficients
for all elements. | src/dewloosh/solid/fem/utils.py | approximation_matrix | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def approximation_matrix(ndf: ndarray, NDOFN: int):
'Returns a matrix of approximation coefficients \n for all elements.'
(nE, nNE) = ndf.shape[:2]
N = (nNE * NDOFN)
nappr = np.eye(N, dtype=ndf.dtype)
res = np.zeros((nE, N, N), dtype=ndf.dtype)
... | @njit(nogil=True, parallel=True, cache=__cache)
def approximation_matrix(ndf: ndarray, NDOFN: int):
'Returns a matrix of approximation coefficients \n for all elements.'
(nE, nNE) = ndf.shape[:2]
N = (nNE * NDOFN)
nappr = np.eye(N, dtype=ndf.dtype)
res = np.zeros((nE, N, N), dtype=ndf.dtype)
... |
bb1162376e4f7e0f3d90fe0a9ffc025e72c4cc3c8e3063523a8e8de90c573780 | @njit(nogil=True, parallel=True, cache=__cache)
def nodal_approximation_matrix(ndf: ndarray):
'Returns a matrix of nodal approximation coefficients \n for all elements.'
(nE, nNE) = ndf.shape[:2]
nappr = np.eye(nNE, dtype=ndf.dtype)
res = np.zeros((nE, nNE, nNE), dtype=ndf.dtype)
for iE in prange... | Returns a matrix of nodal approximation coefficients
for all elements. | src/dewloosh/solid/fem/utils.py | nodal_approximation_matrix | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def nodal_approximation_matrix(ndf: ndarray):
'Returns a matrix of nodal approximation coefficients \n for all elements.'
(nE, nNE) = ndf.shape[:2]
nappr = np.eye(nNE, dtype=ndf.dtype)
res = np.zeros((nE, nNE, nNE), dtype=ndf.dtype)
for iE in prange... | @njit(nogil=True, parallel=True, cache=__cache)
def nodal_approximation_matrix(ndf: ndarray):
'Returns a matrix of nodal approximation coefficients \n for all elements.'
(nE, nNE) = ndf.shape[:2]
nappr = np.eye(nNE, dtype=ndf.dtype)
res = np.zeros((nE, nNE, nNE), dtype=ndf.dtype)
for iE in prange... |
50bba6f3ce2e670abd4d6d06e117075ac97ab0f92b6898a4dbbb5f28638e3cb0 | @njit(nogil=True, parallel=True, cache=__cache)
def compatibility_factors_to_coo(ncf: dict, nreg: dict):
'\n ncf : nodal_compatibility_factors\n '
nN = len(ncf)
widths = np.zeros(nN, dtype=np.int32)
for iN in prange(nN):
widths[iN] = len(nreg[iN])
shapes = (widths ** 2).astype(np.int64... | ncf : nodal_compatibility_factors | src/dewloosh/solid/fem/utils.py | compatibility_factors_to_coo | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def compatibility_factors_to_coo(ncf: dict, nreg: dict):
'\n \n '
nN = len(ncf)
widths = np.zeros(nN, dtype=np.int32)
for iN in prange(nN):
widths[iN] = len(nreg[iN])
shapes = (widths ** 2).astype(np.int64)
N = np.sum(shapes)
data... | @njit(nogil=True, parallel=True, cache=__cache)
def compatibility_factors_to_coo(ncf: dict, nreg: dict):
'\n \n '
nN = len(ncf)
widths = np.zeros(nN, dtype=np.int32)
for iN in prange(nN):
widths[iN] = len(nreg[iN])
shapes = (widths ** 2).astype(np.int64)
N = np.sum(shapes)
data... |
0c61a6c053f3a63737233d8617a4148f4284f97f295b7c2b5fbd93ff2c1c4729 | @njit(nogil=True, cache=__cache)
def compatibility_factors(ncf: dict, nreg: dict, NDOFN: int):
'ncf : nodal_compatibility_factors'
nN = len(ncf)
widths = np.zeros(nN, dtype=np.int32)
for iN in prange(nN):
widths[iN] = len(nreg[iN])
cf = dict()
reg = dict()
for iN in range(nN):
... | ncf : nodal_compatibility_factors | src/dewloosh/solid/fem/utils.py | compatibility_factors | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, cache=__cache)
def compatibility_factors(ncf: dict, nreg: dict, NDOFN: int):
nN = len(ncf)
widths = np.zeros(nN, dtype=np.int32)
for iN in prange(nN):
widths[iN] = len(nreg[iN])
cf = dict()
reg = dict()
for iN in range(nN):
cf[iN] = ncf_to_cf(ncf[iN], NDOFN... | @njit(nogil=True, cache=__cache)
def compatibility_factors(ncf: dict, nreg: dict, NDOFN: int):
nN = len(ncf)
widths = np.zeros(nN, dtype=np.int32)
for iN in prange(nN):
widths[iN] = len(nreg[iN])
cf = dict()
reg = dict()
for iN in range(nN):
cf[iN] = ncf_to_cf(ncf[iN], NDOFN... |
706aa2b6ffb94bd653c911ed39855ed46ac16eaf17924b812a118270a88a20b2 | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_in(A: ndarray, Q: ndarray):
"\n Transforms element vectors (like the load vector) from global to local.\n \n Parameters\n ----------\n A : 3d NumPy float array of shape (nE, nEVAB)\n Array of coefficients to transform.\n \n... | Transforms element vectors (like the load vector) from global to local.
Parameters
----------
A : 3d NumPy float array of shape (nE, nEVAB)
Array of coefficients to transform.
Q : 3d NumPy float array of shape (nE, nEVAB, nEVAB)
Transformation matrices.
Returns
-------
numpy array
NumPy array wit... | src/dewloosh/solid/fem/utils.py | tr_cells_1d_in | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_in(A: ndarray, Q: ndarray):
"\n Transforms element vectors (like the load vector) from global to local.\n \n Parameters\n ----------\n A : 3d NumPy float array of shape (nE, nEVAB)\n Array of coefficients to transform.\n \n... | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_in(A: ndarray, Q: ndarray):
"\n Transforms element vectors (like the load vector) from global to local.\n \n Parameters\n ----------\n A : 3d NumPy float array of shape (nE, nEVAB)\n Array of coefficients to transform.\n \n... |
bce8c807b472bea6a1b0d87b03a53f26887ca90397bcac4891f2db9a591ffef6 | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_out(A: ndarray, Q: ndarray):
'\n Transforms element vectors (like the load vector) from local to global.\n (nE, nNE * nDOF)\n '
res = np.zeros_like(A)
for iE in prange(res.shape[0]):
res[iE] = (Q[iE].T @ A[iE])
return res | Transforms element vectors (like the load vector) from local to global.
(nE, nNE * nDOF) | src/dewloosh/solid/fem/utils.py | tr_cells_1d_out | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_out(A: ndarray, Q: ndarray):
'\n Transforms element vectors (like the load vector) from local to global.\n (nE, nNE * nDOF)\n '
res = np.zeros_like(A)
for iE in prange(res.shape[0]):
res[iE] = (Q[iE].T @ A[iE])
return res | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_out(A: ndarray, Q: ndarray):
'\n Transforms element vectors (like the load vector) from local to global.\n (nE, nNE * nDOF)\n '
res = np.zeros_like(A)
for iE in prange(res.shape[0]):
res[iE] = (Q[iE].T @ A[iE])
return res<|... |
460b282299374f7cc353a2e9f88b218b4cd891e8d568192afa140274d47d28f8 | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_in_multi(A: ndarray, Q: ndarray):
'\n Transforms element vectors (like the load vector) from local to global\n for multiple cases.\n (nE, nRHS, nNE * nDOF)\n '
res = np.zeros_like(A)
for iE in prange(res.shape[0]):
for jRHS ... | Transforms element vectors (like the load vector) from local to global
for multiple cases.
(nE, nRHS, nNE * nDOF) | src/dewloosh/solid/fem/utils.py | tr_cells_1d_in_multi | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_in_multi(A: ndarray, Q: ndarray):
'\n Transforms element vectors (like the load vector) from local to global\n for multiple cases.\n (nE, nRHS, nNE * nDOF)\n '
res = np.zeros_like(A)
for iE in prange(res.shape[0]):
for jRHS ... | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_in_multi(A: ndarray, Q: ndarray):
'\n Transforms element vectors (like the load vector) from local to global\n for multiple cases.\n (nE, nRHS, nNE * nDOF)\n '
res = np.zeros_like(A)
for iE in prange(res.shape[0]):
for jRHS ... |
570c402766b5f549f62664a6ebaa6511b6fd8505b7c4a3ad7f4732f4fa7b53ac | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_out_multi(A: ndarray, Q: ndarray):
'\n Transforms element vectors (like the load vector) from local to global\n for multiple cases.\n A (nE, nRHS, nP * nDOF)\n Q (nE, nP * nDOF)\n '
res = np.zeros_like(A)
for iE in prange(res.sha... | Transforms element vectors (like the load vector) from local to global
for multiple cases.
A (nE, nRHS, nP * nDOF)
Q (nE, nP * nDOF) | src/dewloosh/solid/fem/utils.py | tr_cells_1d_out_multi | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_out_multi(A: ndarray, Q: ndarray):
'\n Transforms element vectors (like the load vector) from local to global\n for multiple cases.\n A (nE, nRHS, nP * nDOF)\n Q (nE, nP * nDOF)\n '
res = np.zeros_like(A)
for iE in prange(res.sha... | @njit(nogil=True, parallel=True, cache=__cache)
def tr_cells_1d_out_multi(A: ndarray, Q: ndarray):
'\n Transforms element vectors (like the load vector) from local to global\n for multiple cases.\n A (nE, nRHS, nP * nDOF)\n Q (nE, nP * nDOF)\n '
res = np.zeros_like(A)
for iE in prange(res.sha... |
48c14f1749031d546f59d593c260eb427c944c049976aa4f83dbb004d69b3537 | @njit(nogil=True, parallel=True, cache=__cache)
def element_dof_solution_bulk(dofsol1d: ndarray, gnum: ndarray):
'\n dofsol (nN * nDOF, nRHS)\n gnum (nE, nEVAB)\n ---\n (nE, nEVAB, nRHS)\n '
nRHS = dofsol1d.shape[1]
(nE, nEVAB) = gnum.shape
res = np.zeros((nE, nEVAB, nRHS), dtype=dofsol1d... | dofsol (nN * nDOF, nRHS)
gnum (nE, nEVAB)
---
(nE, nEVAB, nRHS) | src/dewloosh/solid/fem/utils.py | element_dof_solution_bulk | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def element_dof_solution_bulk(dofsol1d: ndarray, gnum: ndarray):
'\n dofsol (nN * nDOF, nRHS)\n gnum (nE, nEVAB)\n ---\n (nE, nEVAB, nRHS)\n '
nRHS = dofsol1d.shape[1]
(nE, nEVAB) = gnum.shape
res = np.zeros((nE, nEVAB, nRHS), dtype=dofsol1d... | @njit(nogil=True, parallel=True, cache=__cache)
def element_dof_solution_bulk(dofsol1d: ndarray, gnum: ndarray):
'\n dofsol (nN * nDOF, nRHS)\n gnum (nE, nEVAB)\n ---\n (nE, nEVAB, nRHS)\n '
nRHS = dofsol1d.shape[1]
(nE, nEVAB) = gnum.shape
res = np.zeros((nE, nEVAB, nRHS), dtype=dofsol1d... |
34020de7c55f02c72a2af25cb36b24d5f702e5b8ade1ddcc44cdc48d8dcae816 | @njit(nogil=True, parallel=True, cache=__cache)
def transform_stiffness(K: ndarray, dcm: ndarray):
'\n Transforms element stiffness matrices from local to global.\n '
res = np.zeros_like(K)
for iE in prange(res.shape[0]):
res[iE] = ((dcm[iE].T @ K[iE]) @ dcm[iE])
return res | Transforms element stiffness matrices from local to global. | src/dewloosh/solid/fem/utils.py | transform_stiffness | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def transform_stiffness(K: ndarray, dcm: ndarray):
'\n \n '
res = np.zeros_like(K)
for iE in prange(res.shape[0]):
res[iE] = ((dcm[iE].T @ K[iE]) @ dcm[iE])
return res | @njit(nogil=True, parallel=True, cache=__cache)
def transform_stiffness(K: ndarray, dcm: ndarray):
'\n \n '
res = np.zeros_like(K)
for iE in prange(res.shape[0]):
res[iE] = ((dcm[iE].T @ K[iE]) @ dcm[iE])
return res<|docstring|>Transforms element stiffness matrices from local to global.<|e... |
08b76a74bd634fcf902a5eba3a3d0b38e82d08cb87b5b5d7f497ca65bc86ef8d | @njit(nogil=True, parallel=True, cache=__cache)
def constrain_local_stiffness_bulk(K: ndarray, factors: ndarray):
'\n Returns the condensed stiffness matrices representing constraints\n on the internal forces of the elements (eg. hinges).\n \n Currently this solution is only able to handle two states, b... | Returns the condensed stiffness matrices representing constraints
on the internal forces of the elements (eg. hinges).
Currently this solution is only able to handle two states, being total free
and being fully constrained. The factors are expected to be numbers between
0 and 1, where dofs with a factor > 0.5 are ass... | src/dewloosh/solid/fem/utils.py | constrain_local_stiffness_bulk | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def constrain_local_stiffness_bulk(K: ndarray, factors: ndarray):
'\n Returns the condensed stiffness matrices representing constraints\n on the internal forces of the elements (eg. hinges).\n \n Currently this solution is only able to handle two states, b... | @njit(nogil=True, parallel=True, cache=__cache)
def constrain_local_stiffness_bulk(K: ndarray, factors: ndarray):
'\n Returns the condensed stiffness matrices representing constraints\n on the internal forces of the elements (eg. hinges).\n \n Currently this solution is only able to handle two states, b... |
9e3a13004692eb0edc689cfebea072f3f3a592d46020c193d501d5c627cf3367 | @njit(nogil=True, parallel=True, cache=__cache)
def internal_forces(K: ndarray, dofsol: ndarray):
'\n Transforms element stiffness matrices from local to global.\n ---\n (nE, nRHS, nEVAB)\n '
(nE, nRHS, nEVAB) = dofsol.shape
res = np.zeros_like(dofsol)
for i in prange(nE):
for j in p... | Transforms element stiffness matrices from local to global.
---
(nE, nRHS, nEVAB) | src/dewloosh/solid/fem/utils.py | internal_forces | dewloosh/dewloosh-solid | 0 | python | @njit(nogil=True, parallel=True, cache=__cache)
def internal_forces(K: ndarray, dofsol: ndarray):
'\n Transforms element stiffness matrices from local to global.\n ---\n (nE, nRHS, nEVAB)\n '
(nE, nRHS, nEVAB) = dofsol.shape
res = np.zeros_like(dofsol)
for i in prange(nE):
for j in p... | @njit(nogil=True, parallel=True, cache=__cache)
def internal_forces(K: ndarray, dofsol: ndarray):
'\n Transforms element stiffness matrices from local to global.\n ---\n (nE, nRHS, nEVAB)\n '
(nE, nRHS, nEVAB) = dofsol.shape
res = np.zeros_like(dofsol)
for i in prange(nE):
for j in p... |
ab4e1f45d511e525a33cf769de7fec4032425e4fd97b5eca45ba6ca0ef5a8301 | def test_get_word_score():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that it is possible to retrieve a score\n from the dictionary and that the score is an integer.\n '
twitter_api = TwitterAPI()
dictionary = {'Batman': 0}
word = 'Batman'
expected1 = 0
expecte... | Author: Karl Lundvall
Date: 2017-11-13
Purpose: Assert that it is possible to retrieve a score
from the dictionary and that the score is an integer. | Product/TrendManager/UnitTests/test_TwitterAPI.py | test_get_word_score | VincentDehaye/recommender-system-liu | 0 | python | def test_get_word_score():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that it is possible to retrieve a score\n from the dictionary and that the score is an integer.\n '
twitter_api = TwitterAPI()
dictionary = {'Batman': 0}
word = 'Batman'
expected1 = 0
expecte... | def test_get_word_score():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that it is possible to retrieve a score\n from the dictionary and that the score is an integer.\n '
twitter_api = TwitterAPI()
dictionary = {'Batman': 0}
word = 'Batman'
expected1 = 0
expecte... |
73783c718407e6c3c5f53cfad0d2c8ad94addf3ab1f66926bda6e7c0d1ed6d04 | def test_format_word():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that words are in lowercase and that\n all non alphabetic or numeric characters gets removed.\n '
twitter_api = TwitterAPI()
expected = 'hej'
observed = twitter_api.format_word("H*E'?J")
assert (obs... | Author: Karl Lundvall
Date: 2017-11-13
Purpose: Assert that words are in lowercase and that
all non alphabetic or numeric characters gets removed. | Product/TrendManager/UnitTests/test_TwitterAPI.py | test_format_word | VincentDehaye/recommender-system-liu | 0 | python | def test_format_word():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that words are in lowercase and that\n all non alphabetic or numeric characters gets removed.\n '
twitter_api = TwitterAPI()
expected = 'hej'
observed = twitter_api.format_word("H*E'?J")
assert (obs... | def test_format_word():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that words are in lowercase and that\n all non alphabetic or numeric characters gets removed.\n '
twitter_api = TwitterAPI()
expected = 'hej'
observed = twitter_api.format_word("H*E'?J")
assert (obs... |
858c1b80a57de8a0b402b767708ef60e5fc15cc53f654ee0387648255bfc3ca1 | def test_load_dict():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that print_dict retrieves a dictionary from the twitter_dataYYYYMMDD.bin.\n '
twitterapi = TwitterAPI()
twitterapi.load_new_dict()
assert (twitterapi.all_words_new is not None) | Author: Karl Lundvall
Date: 2017-11-13
Purpose: Assert that print_dict retrieves a dictionary from the twitter_dataYYYYMMDD.bin. | Product/TrendManager/UnitTests/test_TwitterAPI.py | test_load_dict | VincentDehaye/recommender-system-liu | 0 | python | def test_load_dict():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that print_dict retrieves a dictionary from the twitter_dataYYYYMMDD.bin.\n '
twitterapi = TwitterAPI()
twitterapi.load_new_dict()
assert (twitterapi.all_words_new is not None) | def test_load_dict():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that print_dict retrieves a dictionary from the twitter_dataYYYYMMDD.bin.\n '
twitterapi = TwitterAPI()
twitterapi.load_new_dict()
assert (twitterapi.all_words_new is not None)<|docstring|>Author: Karl Lundv... |
3fc1c2569754eb653e0dc7a55411b65e503172f47c5f881aa78ffb34299feb6f | def test_get_twitter_score():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that get_twitter_score retrieves a score.\n '
twitter_api = TwitterAPI()
observed = twitter_api.get_twitter_score('rt')
assert (observed > 0) | Author: Karl Lundvall
Date: 2017-11-13
Purpose: Assert that get_twitter_score retrieves a score. | Product/TrendManager/UnitTests/test_TwitterAPI.py | test_get_twitter_score | VincentDehaye/recommender-system-liu | 0 | python | def test_get_twitter_score():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that get_twitter_score retrieves a score.\n '
twitter_api = TwitterAPI()
observed = twitter_api.get_twitter_score('rt')
assert (observed > 0) | def test_get_twitter_score():
'\n Author: Karl Lundvall\n Date: 2017-11-13\n Purpose: Assert that get_twitter_score retrieves a score.\n '
twitter_api = TwitterAPI()
observed = twitter_api.get_twitter_score('rt')
assert (observed > 0)<|docstring|>Author: Karl Lundvall
Date: 2017-11-13
Purpos... |
427cd5754ad8eafd50de6c5cf9e60bb69e46549e44987f4d49b56962aee4f1a5 | def test_get_newest_file():
'\n Author: Albin Bergvall\n Date: 2017-11-20\n Purpose: Assert that get_newest_file returns a file\n from the twitterdata directory, if the file exists\n '
twitter_api = TwitterAPI()
observed = twitter_api.get_newest_file()
assert (observed is not None) | Author: Albin Bergvall
Date: 2017-11-20
Purpose: Assert that get_newest_file returns a file
from the twitterdata directory, if the file exists | Product/TrendManager/UnitTests/test_TwitterAPI.py | test_get_newest_file | VincentDehaye/recommender-system-liu | 0 | python | def test_get_newest_file():
'\n Author: Albin Bergvall\n Date: 2017-11-20\n Purpose: Assert that get_newest_file returns a file\n from the twitterdata directory, if the file exists\n '
twitter_api = TwitterAPI()
observed = twitter_api.get_newest_file()
assert (observed is not None) | def test_get_newest_file():
'\n Author: Albin Bergvall\n Date: 2017-11-20\n Purpose: Assert that get_newest_file returns a file\n from the twitterdata directory, if the file exists\n '
twitter_api = TwitterAPI()
observed = twitter_api.get_newest_file()
assert (observed is not None)<|docst... |
8da3c21e201f483b83b14fc6a6d7fedf8d7ac1eb4bdc8a86c3f5d504886e511b | def setup(self, bottom, top):
'Setup the RoIDataLayer.'
layer_params = yaml.load(self.param_str)
self._num_regions = cfg.TRAIN.num_regions
self._num_regions_one_side = int(np.sqrt(self._num_regions))
self._num_classes = (layer_params['num_classes'] - 1)
self._agnostic_box = layer_params['agnosti... | Setup the RoIDataLayer. | lib/roi_data_layer/rfcn_anno_layer_new.py | setup | jialinwu17/box_cls_reg | 0 | python | def setup(self, bottom, top):
layer_params = yaml.load(self.param_str)
self._num_regions = cfg.TRAIN.num_regions
self._num_regions_one_side = int(np.sqrt(self._num_regions))
self._num_classes = (layer_params['num_classes'] - 1)
self._agnostic_box = layer_params['agnostic_box']
if self._agno... | def setup(self, bottom, top):
layer_params = yaml.load(self.param_str)
self._num_regions = cfg.TRAIN.num_regions
self._num_regions_one_side = int(np.sqrt(self._num_regions))
self._num_classes = (layer_params['num_classes'] - 1)
self._agnostic_box = layer_params['agnostic_box']
if self._agno... |
82e3a4184e557ca5b5abf495b2c3c9d65f3531837d53247231e304d742bd0df3 | def forward(self, bottom, top):
"Get blobs and copy them into this layer's top blob vector."
rois = bottom[1].data
rfcn_feat = bottom[2].data
self._num_rois = bottom[1].data.shape[0]
rfcn_conf = bottom[0].data
seed_points = np.zeros(((self._num_rois * cfg.TRAIN.M), 2))
seed_points_feat = np.... | Get blobs and copy them into this layer's top blob vector. | lib/roi_data_layer/rfcn_anno_layer_new.py | forward | jialinwu17/box_cls_reg | 0 | python | def forward(self, bottom, top):
rois = bottom[1].data
rfcn_feat = bottom[2].data
self._num_rois = bottom[1].data.shape[0]
rfcn_conf = bottom[0].data
seed_points = np.zeros(((self._num_rois * cfg.TRAIN.M), 2))
seed_points_feat = np.zeros(((self._num_rois * cfg.TRAIN.M), cfg.TRAIN.num_feature... | def forward(self, bottom, top):
rois = bottom[1].data
rfcn_feat = bottom[2].data
self._num_rois = bottom[1].data.shape[0]
rfcn_conf = bottom[0].data
seed_points = np.zeros(((self._num_rois * cfg.TRAIN.M), 2))
seed_points_feat = np.zeros(((self._num_rois * cfg.TRAIN.M), cfg.TRAIN.num_feature... |
f0bb8e4bfeb0cac29fcadb7c5fdaed5b3d19ffc491ba6bda79983501befd2aa2 | def backward(self, top, propagate_down, bottom):
'This layer does not propagate gradients.'
pass | This layer does not propagate gradients. | lib/roi_data_layer/rfcn_anno_layer_new.py | backward | jialinwu17/box_cls_reg | 0 | python | def backward(self, top, propagate_down, bottom):
pass | def backward(self, top, propagate_down, bottom):
pass<|docstring|>This layer does not propagate gradients.<|endoftext|> |
a5f59d623aef4dd060181c592741c81f45856de3d73c8d9185955a625623bec1 | def reshape(self, bottom, top):
'Reshaping happens during the call to forward.'
pass | Reshaping happens during the call to forward. | lib/roi_data_layer/rfcn_anno_layer_new.py | reshape | jialinwu17/box_cls_reg | 0 | python | def reshape(self, bottom, top):
pass | def reshape(self, bottom, top):
pass<|docstring|>Reshaping happens during the call to forward.<|endoftext|> |
e86d921c5c24c604eb8a798966d18d4dbdf60285fe5143da1afecfd60d8c801e | def l2_optimality_error(self, params, *args, **kwargs):
'Computes the L2 optimality error.'
optimality = self.optimality_fun(params, *args, **kwargs)
return tree_util.tree_l2_norm(optimality) | Computes the L2 optimality error. | jaxopt/_src/base.py | l2_optimality_error | gowerrobert/jaxopt | 0 | python | def l2_optimality_error(self, params, *args, **kwargs):
optimality = self.optimality_fun(params, *args, **kwargs)
return tree_util.tree_l2_norm(optimality) | def l2_optimality_error(self, params, *args, **kwargs):
optimality = self.optimality_fun(params, *args, **kwargs)
return tree_util.tree_l2_norm(optimality)<|docstring|>Computes the L2 optimality error.<|endoftext|> |
ebc91526557c021da284e940f56c49a63cb6f9f753f214085674522dd0a7482d | def run(self, init_params: Any, *args, **kwargs) -> OptStep:
'Runs the solver until convergence or `maxiter` is reached.\n\n Args:\n init_params: pytree containing the initial parameters.\n *args: additional positional arguments to be passed to the update method.\n **kwargs: additional keyword arg... | Runs the solver until convergence or `maxiter` is reached.
Args:
init_params: pytree containing the initial parameters.
*args: additional positional arguments to be passed to the update method.
**kwargs: additional keyword arguments to be passed to the update method.
Return type:
OptStep
Returns:
(params, st... | jaxopt/_src/base.py | run | gowerrobert/jaxopt | 0 | python | def run(self, init_params: Any, *args, **kwargs) -> OptStep:
'Runs the solver until convergence or `maxiter` is reached.\n\n Args:\n init_params: pytree containing the initial parameters.\n *args: additional positional arguments to be passed to the update method.\n **kwargs: additional keyword arg... | def run(self, init_params: Any, *args, **kwargs) -> OptStep:
'Runs the solver until convergence or `maxiter` is reached.\n\n Args:\n init_params: pytree containing the initial parameters.\n *args: additional positional arguments to be passed to the update method.\n **kwargs: additional keyword arg... |
f4dbc16fb424b7e71e3d8e98ad0fe60d6abc64bc79fd64ede1842ca99da30e4a | def run_iterator(self, init_params: Any, iterator, *args, **kwargs) -> OptStep:
'Runs the solver on a dataset iterator until `maxiter` is reached.\n\n Args:\n init_params: pytree containing the initial parameters.\n iterator: iterator generating data batches.\n *args: additional positional argumen... | Runs the solver on a dataset iterator until `maxiter` is reached.
Args:
init_params: pytree containing the initial parameters.
iterator: iterator generating data batches.
*args: additional positional arguments to be passed to ``fun``.
**kwargs: additional keyword arguments to be passed to ``fun``.
Return type:... | jaxopt/_src/base.py | run_iterator | gowerrobert/jaxopt | 0 | python | def run_iterator(self, init_params: Any, iterator, *args, **kwargs) -> OptStep:
'Runs the solver on a dataset iterator until `maxiter` is reached.\n\n Args:\n init_params: pytree containing the initial parameters.\n iterator: iterator generating data batches.\n *args: additional positional argumen... | def run_iterator(self, init_params: Any, iterator, *args, **kwargs) -> OptStep:
'Runs the solver on a dataset iterator until `maxiter` is reached.\n\n Args:\n init_params: pytree containing the initial parameters.\n iterator: iterator generating data batches.\n *args: additional positional argumen... |
5a7e3b12370608e1bfc6da3ab161084b41f88d27ccab1b4c80899ff89104aa00 | def matvec(self, x):
'Computes dot(A, x).'
return jnp.dot(self.A, x) | Computes dot(A, x). | jaxopt/_src/base.py | matvec | gowerrobert/jaxopt | 0 | python | def matvec(self, x):
return jnp.dot(self.A, x) | def matvec(self, x):
return jnp.dot(self.A, x)<|docstring|>Computes dot(A, x).<|endoftext|> |
073ce50e032a89cf76cf1b672583bf624b7215f954ba5bf51eb8ba9813ade9ab | def matvec_element(self, x, idx):
'Computes dot(A, x)[idx].'
return jnp.dot(self.A[idx], x) | Computes dot(A, x)[idx]. | jaxopt/_src/base.py | matvec_element | gowerrobert/jaxopt | 0 | python | def matvec_element(self, x, idx):
return jnp.dot(self.A[idx], x) | def matvec_element(self, x, idx):
return jnp.dot(self.A[idx], x)<|docstring|>Computes dot(A, x)[idx].<|endoftext|> |
264c906dc4c71cee9e5b340469295189c8056f575dfb487ddad526de1f5599eb | def rmatvec(self, x):
'Computes dot(A.T, x).'
return jnp.dot(self.A.T, x) | Computes dot(A.T, x). | jaxopt/_src/base.py | rmatvec | gowerrobert/jaxopt | 0 | python | def rmatvec(self, x):
return jnp.dot(self.A.T, x) | def rmatvec(self, x):
return jnp.dot(self.A.T, x)<|docstring|>Computes dot(A.T, x).<|endoftext|> |
97d58cc7a69f3809428de5547b8299dd3ce6910b1b464565680a479b094d4393 | def rmatvec_element(self, x, idx):
'Computes dot(A.T, x)[idx].'
return jnp.dot(self.A[(:, idx)], x) | Computes dot(A.T, x)[idx]. | jaxopt/_src/base.py | rmatvec_element | gowerrobert/jaxopt | 0 | python | def rmatvec_element(self, x, idx):
return jnp.dot(self.A[(:, idx)], x) | def rmatvec_element(self, x, idx):
return jnp.dot(self.A[(:, idx)], x)<|docstring|>Computes dot(A.T, x)[idx].<|endoftext|> |
961fd69285e0f6de8272c2f8b82fbda50b8f280a3755080e08753b8673f17b3d | def update_matvec(self, Ax, delta, idx):
'Updates dot(A, x) when x[idx] += delta.'
if (len(Ax.shape) == 1):
return (Ax + (delta * self.A[(:, idx)]))
elif (len(Ax.shape) == 2):
return (Ax + jnp.outer(self.A[(:, idx)], delta))
else:
raise ValueError('Ax should be a vector or a matr... | Updates dot(A, x) when x[idx] += delta. | jaxopt/_src/base.py | update_matvec | gowerrobert/jaxopt | 0 | python | def update_matvec(self, Ax, delta, idx):
if (len(Ax.shape) == 1):
return (Ax + (delta * self.A[(:, idx)]))
elif (len(Ax.shape) == 2):
return (Ax + jnp.outer(self.A[(:, idx)], delta))
else:
raise ValueError('Ax should be a vector or a matrix.') | def update_matvec(self, Ax, delta, idx):
if (len(Ax.shape) == 1):
return (Ax + (delta * self.A[(:, idx)]))
elif (len(Ax.shape) == 2):
return (Ax + jnp.outer(self.A[(:, idx)], delta))
else:
raise ValueError('Ax should be a vector or a matrix.')<|docstring|>Updates dot(A, x) when ... |
f8eb385eeeeb04837c30f37dc717cf604cb7bbd8dc7a953f2560b857b141b63a | def update_rmatvec(self, ATx, delta, idx):
'Updates dot(A.T, x) when x[idx] += delta.'
if (len(ATx.shape) == 1):
return (ATx + (delta * self.A[idx]))
elif (len(ATx.shape) == 2):
raise NotImplementedError
else:
raise ValueError('Ax should be a vector or a matrix.') | Updates dot(A.T, x) when x[idx] += delta. | jaxopt/_src/base.py | update_rmatvec | gowerrobert/jaxopt | 0 | python | def update_rmatvec(self, ATx, delta, idx):
if (len(ATx.shape) == 1):
return (ATx + (delta * self.A[idx]))
elif (len(ATx.shape) == 2):
raise NotImplementedError
else:
raise ValueError('Ax should be a vector or a matrix.') | def update_rmatvec(self, ATx, delta, idx):
if (len(ATx.shape) == 1):
return (ATx + (delta * self.A[idx]))
elif (len(ATx.shape) == 2):
raise NotImplementedError
else:
raise ValueError('Ax should be a vector or a matrix.')<|docstring|>Updates dot(A.T, x) when x[idx] += delta.<|end... |
0a45d67eea0323f499ef8884af15dbc0d730f942a7c6fa5dd8f01efe2164e880 | def create_user(self, email, password=None, **kwargs):
'Create and return a `User` with an email and password.'
if (email is None):
raise TypeError('Users must have an email address.')
normalized_email = self.normalize_email(email)
username = normalized_email.split('@')[0]
kwargs['username']... | Create and return a `User` with an email and password. | authentication/models.py | create_user | RetroFlow/retro-flow | 0 | python | def create_user(self, email, password=None, **kwargs):
if (email is None):
raise TypeError('Users must have an email address.')
normalized_email = self.normalize_email(email)
username = normalized_email.split('@')[0]
kwargs['username'] = (kwargs.get('username') or username)
user = self.... | def create_user(self, email, password=None, **kwargs):
if (email is None):
raise TypeError('Users must have an email address.')
normalized_email = self.normalize_email(email)
username = normalized_email.split('@')[0]
kwargs['username'] = (kwargs.get('username') or username)
user = self.... |
bbae092fd7eba3103bb5e5457194994ade7d57e75a02c1dd885da477f5ff3a20 | def create_superuser(self, email, password, **kwargs):
'\n Create and return a `User` with superuser (admin) permissions.\n '
if (password is None):
raise TypeError('Superusers must have a password.')
user = self.create_user(email, password, **kwargs)
user.is_superuser = True
u... | Create and return a `User` with superuser (admin) permissions. | authentication/models.py | create_superuser | RetroFlow/retro-flow | 0 | python | def create_superuser(self, email, password, **kwargs):
'\n \n '
if (password is None):
raise TypeError('Superusers must have a password.')
user = self.create_user(email, password, **kwargs)
user.is_superuser = True
user.is_staff = True
user.save()
return user | def create_superuser(self, email, password, **kwargs):
'\n \n '
if (password is None):
raise TypeError('Superusers must have a password.')
user = self.create_user(email, password, **kwargs)
user.is_superuser = True
user.is_staff = True
user.save()
return user<|docstring... |
ce52496434047a2506e1537ba004076ee35cc4678912654b1b93a5c1338706c0 | def __str__(self):
'\n Returns a string representation of this `User`.\n\n This string is used when a `User` is printed in the console.\n '
return self.email | Returns a string representation of this `User`.
This string is used when a `User` is printed in the console. | authentication/models.py | __str__ | RetroFlow/retro-flow | 0 | python | def __str__(self):
'\n Returns a string representation of this `User`.\n\n This string is used when a `User` is printed in the console.\n '
return self.email | def __str__(self):
'\n Returns a string representation of this `User`.\n\n This string is used when a `User` is printed in the console.\n '
return self.email<|docstring|>Returns a string representation of this `User`.
This string is used when a `User` is printed in the console.<|endoftext|... |
9f840d32a404226e21f6172a633a7a881bddf1ce5744c1f2f30b930e489d7e42 | @property
def token(self):
'\n Allows us to get a user\'s token by calling `user.token` instead of\n `user.generate_jwt_token().\n\n The `@property` decorator above makes this possible. `token` is called\n a "dynamic property".\n '
return self._generate_jwt_token() | Allows us to get a user's token by calling `user.token` instead of
`user.generate_jwt_token().
The `@property` decorator above makes this possible. `token` is called
a "dynamic property". | authentication/models.py | token | RetroFlow/retro-flow | 0 | python | @property
def token(self):
'\n Allows us to get a user\'s token by calling `user.token` instead of\n `user.generate_jwt_token().\n\n The `@property` decorator above makes this possible. `token` is called\n a "dynamic property".\n '
return self._generate_jwt_token() | @property
def token(self):
'\n Allows us to get a user\'s token by calling `user.token` instead of\n `user.generate_jwt_token().\n\n The `@property` decorator above makes this possible. `token` is called\n a "dynamic property".\n '
return self._generate_jwt_token()<|docstring|... |
47c2fc5c2e880b63a42b60c398cb6ea9c1c3b659194c6974861d33f082ba21a2 | def get_full_name(self):
"\n This method is required by Django for things like handling emails.\n Typically this would be the user's first and last name.\n "
return self.username | This method is required by Django for things like handling emails.
Typically this would be the user's first and last name. | authentication/models.py | get_full_name | RetroFlow/retro-flow | 0 | python | def get_full_name(self):
"\n This method is required by Django for things like handling emails.\n Typically this would be the user's first and last name.\n "
return self.username | def get_full_name(self):
"\n This method is required by Django for things like handling emails.\n Typically this would be the user's first and last name.\n "
return self.username<|docstring|>This method is required by Django for things like handling emails.
Typically this would be the user'... |
111d26353f75acbc68a5edddce397bb0f1cec0cfb26546c5e25ebacd6f703913 | def get_short_name(self):
"\n This method is required by Django for things like handling emails.\n Typically, this would be the user's first name. Since we do not store\n the user's real name, we return their username instead.\n "
return self.username | This method is required by Django for things like handling emails.
Typically, this would be the user's first name. Since we do not store
the user's real name, we return their username instead. | authentication/models.py | get_short_name | RetroFlow/retro-flow | 0 | python | def get_short_name(self):
"\n This method is required by Django for things like handling emails.\n Typically, this would be the user's first name. Since we do not store\n the user's real name, we return their username instead.\n "
return self.username | def get_short_name(self):
"\n This method is required by Django for things like handling emails.\n Typically, this would be the user's first name. Since we do not store\n the user's real name, we return their username instead.\n "
return self.username<|docstring|>This method is requi... |
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