Dataset Viewer
hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d99e8a9a95f28da6c2d4d1ee42e95a270ab08977
| 421 |
py
|
Python
|
coding_intereview/1475. Final Prices With a Special Discount in a Shop.py
|
Jahidul007/Python-Bootcamp
|
3c870587465ff66c2c1871c8d3c4eea72463abda
|
[
"MIT"
] | 2 |
2020-12-07T16:07:07.000Z
|
2020-12-07T16:08:53.000Z
|
coding_intereview/1475. Final Prices With a Special Discount in a Shop.py
|
purusharthmalik/Python-Bootcamp
|
2ed1cf886d1081de200b0fdd4cb4e28008c7e3d1
|
[
"MIT"
] | null | null | null |
coding_intereview/1475. Final Prices With a Special Discount in a Shop.py
|
purusharthmalik/Python-Bootcamp
|
2ed1cf886d1081de200b0fdd4cb4e28008c7e3d1
|
[
"MIT"
] | 1 |
2020-10-03T16:38:02.000Z
|
2020-10-03T16:38:02.000Z
|
class Solution:
def finalPrices(self, prices: List[int]) -> List[int]:
res = []
for i in range(len(prices)):
for j in range(i+1,len(prices)):
if prices[j]<=prices[i]:
res.append(prices[i]-prices[j])
break
if j==len(prices)-1:
res.append(prices[i])
res.append(prices[-1])
return res
| 35.083333 | 58 | 0.460808 | 52 | 421 | 3.730769 | 0.384615 | 0.139175 | 0.231959 | 0.164948 | 0.226804 | 0 | 0 | 0 | 0 | 0 | 0 | 0.011858 | 0.39905 | 421 | 12 | 59 | 35.083333 | 0.754941 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.083333 | false | 0 | 0 | 0 | 0.25 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0
| 2 |
d9a714b3484177f5fee5427d98c53a86bf48daf3
| 134 |
py
|
Python
|
tests/__init__.py
|
eloo/sensor.sbahn_munich
|
05e05a845178ab529dc4c80e924035fe1d072b55
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
eloo/sensor.sbahn_munich
|
05e05a845178ab529dc4c80e924035fe1d072b55
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
eloo/sensor.sbahn_munich
|
05e05a845178ab529dc4c80e924035fe1d072b55
|
[
"MIT"
] | null | null | null |
"""Tests for the sbahn_munich integration"""
line_dict = {
"name": "S3",
"color": "#333333",
"text_color": "#444444",
}
| 14.888889 | 44 | 0.567164 | 15 | 134 | 4.866667 | 0.933333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.12381 | 0.216418 | 134 | 8 | 45 | 16.75 | 0.571429 | 0.283582 | 0 | 0 | 0 | 0 | 0.388889 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0
| 2 |
d9c310055166d8d1507c05ad91c6bc47af7f5743
| 32,544 |
py
|
Python
|
dungeoncog/enemy_skills_pb2.py
|
muffin-rice/pad-cogs
|
820ecf08f9569a3d7cf3264d0eb9567264b42edf
|
[
"MIT"
] | 3 |
2021-04-16T23:47:59.000Z
|
2021-09-10T06:00:18.000Z
|
dungeoncog/enemy_skills_pb2.py
|
muffin-rice/pad-cogs
|
820ecf08f9569a3d7cf3264d0eb9567264b42edf
|
[
"MIT"
] | 708 |
2020-10-31T08:02:40.000Z
|
2022-03-31T09:39:25.000Z
|
dungeoncog/enemy_skills_pb2.py
|
muffin-rice/pad-cogs
|
820ecf08f9569a3d7cf3264d0eb9567264b42edf
|
[
"MIT"
] | 20 |
2020-11-01T23:11:29.000Z
|
2022-02-07T07:04:15.000Z
|
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: enemy_skills.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='enemy_skills.proto',
package='dadguide_proto',
syntax='proto3',
serialized_options=None,
create_key=_descriptor._internal_create_key,
serialized_pb=b'\n\x12\x65nemy_skills.proto\x12\x0e\x64\x61\x64guide_proto\"\xbf\x02\n\x1cMonsterBehaviorWithOverrides\x12\x12\n\nmonster_id\x18\x01 \x01(\x05\x12-\n\x06levels\x18\x02 \x03(\x0b\x32\x1d.dadguide_proto.LevelBehavior\x12\x36\n\x0flevel_overrides\x18\x03 \x03(\x0b\x32\x1d.dadguide_proto.LevelBehavior\x12\x43\n\x06status\x18\x04 \x01(\x0e\x32\x33.dadguide_proto.MonsterBehaviorWithOverrides.Status\"_\n\x06Status\x12\x10\n\x0cNOT_APPROVED\x10\x00\x12\x12\n\x0e\x41PPROVED_AS_IS\x10\x01\x12\x14\n\x10NEEDS_REAPPROVAL\x10\x02\x12\x19\n\x15\x41PPROVED_WITH_CHANGES\x10\x03\"f\n\x0fMonsterBehavior\x12\x12\n\nmonster_id\x18\x01 \x01(\x05\x12-\n\x06levels\x18\x02 \x03(\x0b\x32\x1d.dadguide_proto.LevelBehavior\x12\x10\n\x08\x61pproved\x18\x03 \x01(\x08\"M\n\rLevelBehavior\x12\r\n\x05level\x18\x01 \x01(\x05\x12-\n\x06groups\x18\x02 \x03(\x0b\x32\x1d.dadguide_proto.BehaviorGroup\"\xd9\x02\n\rBehaviorGroup\x12;\n\ngroup_type\x18\x01 \x01(\x0e\x32\'.dadguide_proto.BehaviorGroup.GroupType\x12,\n\tcondition\x18\x02 \x01(\x0b\x32\x19.dadguide_proto.Condition\x12.\n\x08\x63hildren\x18\x03 \x03(\x0b\x32\x1c.dadguide_proto.BehaviorItem\"\xac\x01\n\tGroupType\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\x0b\n\x07PASSIVE\x10\x01\x12\x0b\n\x07PREEMPT\x10\x02\x12\x11\n\rDISPEL_PLAYER\x10\x03\x12\x12\n\x0eMONSTER_STATUS\x10\x04\x12\r\n\tREMAINING\x10\x05\x12\x0c\n\x08STANDARD\x10\x06\x12\t\n\x05\x44\x45\x41TH\x10\x07\x12\x0f\n\x0bUNKNOWN_USE\x10\x08\x12\x14\n\x10HIGHEST_PRIORITY\x10\t\"u\n\x0c\x42\x65haviorItem\x12.\n\x05group\x18\x02 \x01(\x0b\x32\x1d.dadguide_proto.BehaviorGroupH\x00\x12,\n\x08\x62\x65havior\x18\x03 \x01(\x0b\x32\x18.dadguide_proto.BehaviorH\x00\x42\x07\n\x05value\"c\n\x08\x42\x65havior\x12,\n\tcondition\x18\x01 \x01(\x0b\x32\x19.dadguide_proto.Condition\x12\x16\n\x0e\x65nemy_skill_id\x18\x02 \x01(\x05\x12\x11\n\tchild_ids\x18\x03 \x03(\x05\"\x80\x04\n\tCondition\x12\x14\n\x0chp_threshold\x18\x01 \x01(\x05\x12\x12\n\nuse_chance\x18\x02 \x01(\x05\x12\x15\n\rrepeats_every\x18\x03 \x01(\x05\x12\x17\n\x0fglobal_one_time\x18\x04 \x01(\x08\x12\x19\n\x11limited_execution\x18\r \x01(\x05\x12!\n\x19trigger_enemies_remaining\x18\x05 \x01(\x05\x12\x13\n\x0bif_defeated\x18\x06 \x01(\x08\x12\x1f\n\x17if_attributes_available\x18\x07 \x01(\x08\x12\x18\n\x10trigger_monsters\x18\x08 \x03(\x05\x12\x16\n\x0etrigger_combos\x18\t \x01(\x05\x12\x1a\n\x12if_nothing_matched\x18\n \x01(\x08\x12\x14\n\x0ctrigger_turn\x18\x0b \x01(\x05\x12\x18\n\x10trigger_turn_end\x18\x0c \x01(\x05\x12\x1c\n\x14\x61lways_trigger_above\x18\x0e \x01(\x05\x12\x14\n\x0c\x61lways_after\x18\x0f \x01(\x05\x12\x11\n\tskill_set\x18\x10 \x01(\x05\x12\x19\n\x11\x65rased_attributes\x18\x11 \x03(\x05\x12\x13\n\x0b\x64\x61mage_done\x18\x12 \x01(\x05\x12\x1b\n\x13\x61ttributes_attacked\x18\x13 \x03(\x05\x12\x13\n\x0bskills_used\x18\x14 \x01(\x05\x62\x06proto3'
)
_MONSTERBEHAVIORWITHOVERRIDES_STATUS = _descriptor.EnumDescriptor(
name='Status',
full_name='dadguide_proto.MonsterBehaviorWithOverrides.Status',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='NOT_APPROVED', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='APPROVED_AS_IS', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='NEEDS_REAPPROVAL', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='APPROVED_WITH_CHANGES', index=3, number=3,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=263,
serialized_end=358,
)
_sym_db.RegisterEnumDescriptor(_MONSTERBEHAVIORWITHOVERRIDES_STATUS)
_BEHAVIORGROUP_GROUPTYPE = _descriptor.EnumDescriptor(
name='GroupType',
full_name='dadguide_proto.BehaviorGroup.GroupType',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='UNSPECIFIED', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='PASSIVE', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='PREEMPT', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='DISPEL_PLAYER', index=3, number=3,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='MONSTER_STATUS', index=4, number=4,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='REMAINING', index=5, number=5,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='STANDARD', index=6, number=6,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='DEATH', index=7, number=7,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='UNKNOWN_USE', index=8, number=8,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='HIGHEST_PRIORITY', index=9, number=9,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=717,
serialized_end=889,
)
_sym_db.RegisterEnumDescriptor(_BEHAVIORGROUP_GROUPTYPE)
_MONSTERBEHAVIORWITHOVERRIDES = _descriptor.Descriptor(
name='MonsterBehaviorWithOverrides',
full_name='dadguide_proto.MonsterBehaviorWithOverrides',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='monster_id', full_name='dadguide_proto.MonsterBehaviorWithOverrides.monster_id', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='levels', full_name='dadguide_proto.MonsterBehaviorWithOverrides.levels', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='level_overrides', full_name='dadguide_proto.MonsterBehaviorWithOverrides.level_overrides', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='status', full_name='dadguide_proto.MonsterBehaviorWithOverrides.status', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
_MONSTERBEHAVIORWITHOVERRIDES_STATUS,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=39,
serialized_end=358,
)
_MONSTERBEHAVIOR = _descriptor.Descriptor(
name='MonsterBehavior',
full_name='dadguide_proto.MonsterBehavior',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='monster_id', full_name='dadguide_proto.MonsterBehavior.monster_id', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='levels', full_name='dadguide_proto.MonsterBehavior.levels', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='approved', full_name='dadguide_proto.MonsterBehavior.approved', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=360,
serialized_end=462,
)
_LEVELBEHAVIOR = _descriptor.Descriptor(
name='LevelBehavior',
full_name='dadguide_proto.LevelBehavior',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='level', full_name='dadguide_proto.LevelBehavior.level', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='groups', full_name='dadguide_proto.LevelBehavior.groups', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=464,
serialized_end=541,
)
_BEHAVIORGROUP = _descriptor.Descriptor(
name='BehaviorGroup',
full_name='dadguide_proto.BehaviorGroup',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='group_type', full_name='dadguide_proto.BehaviorGroup.group_type', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='condition', full_name='dadguide_proto.BehaviorGroup.condition', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='children', full_name='dadguide_proto.BehaviorGroup.children', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
_BEHAVIORGROUP_GROUPTYPE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=544,
serialized_end=889,
)
_BEHAVIORITEM = _descriptor.Descriptor(
name='BehaviorItem',
full_name='dadguide_proto.BehaviorItem',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='group', full_name='dadguide_proto.BehaviorItem.group', index=0,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='behavior', full_name='dadguide_proto.BehaviorItem.behavior', index=1,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name='value', full_name='dadguide_proto.BehaviorItem.value',
index=0, containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[]),
],
serialized_start=891,
serialized_end=1008,
)
_BEHAVIOR = _descriptor.Descriptor(
name='Behavior',
full_name='dadguide_proto.Behavior',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='condition', full_name='dadguide_proto.Behavior.condition', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='enemy_skill_id', full_name='dadguide_proto.Behavior.enemy_skill_id', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='child_ids', full_name='dadguide_proto.Behavior.child_ids', index=2,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1010,
serialized_end=1109,
)
_CONDITION = _descriptor.Descriptor(
name='Condition',
full_name='dadguide_proto.Condition',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='hp_threshold', full_name='dadguide_proto.Condition.hp_threshold', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='use_chance', full_name='dadguide_proto.Condition.use_chance', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='repeats_every', full_name='dadguide_proto.Condition.repeats_every', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='global_one_time', full_name='dadguide_proto.Condition.global_one_time', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='limited_execution', full_name='dadguide_proto.Condition.limited_execution', index=4,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_enemies_remaining', full_name='dadguide_proto.Condition.trigger_enemies_remaining', index=5,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='if_defeated', full_name='dadguide_proto.Condition.if_defeated', index=6,
number=6, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='if_attributes_available', full_name='dadguide_proto.Condition.if_attributes_available', index=7,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_monsters', full_name='dadguide_proto.Condition.trigger_monsters', index=8,
number=8, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_combos', full_name='dadguide_proto.Condition.trigger_combos', index=9,
number=9, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='if_nothing_matched', full_name='dadguide_proto.Condition.if_nothing_matched', index=10,
number=10, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_turn', full_name='dadguide_proto.Condition.trigger_turn', index=11,
number=11, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_turn_end', full_name='dadguide_proto.Condition.trigger_turn_end', index=12,
number=12, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='always_trigger_above', full_name='dadguide_proto.Condition.always_trigger_above', index=13,
number=14, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='always_after', full_name='dadguide_proto.Condition.always_after', index=14,
number=15, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='skill_set', full_name='dadguide_proto.Condition.skill_set', index=15,
number=16, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='erased_attributes', full_name='dadguide_proto.Condition.erased_attributes', index=16,
number=17, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='damage_done', full_name='dadguide_proto.Condition.damage_done', index=17,
number=18, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='attributes_attacked', full_name='dadguide_proto.Condition.attributes_attacked', index=18,
number=19, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='skills_used', full_name='dadguide_proto.Condition.skills_used', index=19,
number=20, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1112,
serialized_end=1624,
)
_MONSTERBEHAVIORWITHOVERRIDES.fields_by_name['levels'].message_type = _LEVELBEHAVIOR
_MONSTERBEHAVIORWITHOVERRIDES.fields_by_name['level_overrides'].message_type = _LEVELBEHAVIOR
_MONSTERBEHAVIORWITHOVERRIDES.fields_by_name['status'].enum_type = _MONSTERBEHAVIORWITHOVERRIDES_STATUS
_MONSTERBEHAVIORWITHOVERRIDES_STATUS.containing_type = _MONSTERBEHAVIORWITHOVERRIDES
_MONSTERBEHAVIOR.fields_by_name['levels'].message_type = _LEVELBEHAVIOR
_LEVELBEHAVIOR.fields_by_name['groups'].message_type = _BEHAVIORGROUP
_BEHAVIORGROUP.fields_by_name['group_type'].enum_type = _BEHAVIORGROUP_GROUPTYPE
_BEHAVIORGROUP.fields_by_name['condition'].message_type = _CONDITION
_BEHAVIORGROUP.fields_by_name['children'].message_type = _BEHAVIORITEM
_BEHAVIORGROUP_GROUPTYPE.containing_type = _BEHAVIORGROUP
_BEHAVIORITEM.fields_by_name['group'].message_type = _BEHAVIORGROUP
_BEHAVIORITEM.fields_by_name['behavior'].message_type = _BEHAVIOR
_BEHAVIORITEM.oneofs_by_name['value'].fields.append(
_BEHAVIORITEM.fields_by_name['group'])
_BEHAVIORITEM.fields_by_name['group'].containing_oneof = _BEHAVIORITEM.oneofs_by_name['value']
_BEHAVIORITEM.oneofs_by_name['value'].fields.append(
_BEHAVIORITEM.fields_by_name['behavior'])
_BEHAVIORITEM.fields_by_name['behavior'].containing_oneof = _BEHAVIORITEM.oneofs_by_name['value']
_BEHAVIOR.fields_by_name['condition'].message_type = _CONDITION
DESCRIPTOR.message_types_by_name['MonsterBehaviorWithOverrides'] = _MONSTERBEHAVIORWITHOVERRIDES
DESCRIPTOR.message_types_by_name['MonsterBehavior'] = _MONSTERBEHAVIOR
DESCRIPTOR.message_types_by_name['LevelBehavior'] = _LEVELBEHAVIOR
DESCRIPTOR.message_types_by_name['BehaviorGroup'] = _BEHAVIORGROUP
DESCRIPTOR.message_types_by_name['BehaviorItem'] = _BEHAVIORITEM
DESCRIPTOR.message_types_by_name['Behavior'] = _BEHAVIOR
DESCRIPTOR.message_types_by_name['Condition'] = _CONDITION
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
MonsterBehaviorWithOverrides = _reflection.GeneratedProtocolMessageType('MonsterBehaviorWithOverrides',
(_message.Message,), {
'DESCRIPTOR': _MONSTERBEHAVIORWITHOVERRIDES,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.MonsterBehaviorWithOverrides)
})
_sym_db.RegisterMessage(MonsterBehaviorWithOverrides)
MonsterBehavior = _reflection.GeneratedProtocolMessageType('MonsterBehavior', (_message.Message,), {
'DESCRIPTOR': _MONSTERBEHAVIOR,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.MonsterBehavior)
})
_sym_db.RegisterMessage(MonsterBehavior)
LevelBehavior = _reflection.GeneratedProtocolMessageType('LevelBehavior', (_message.Message,), {
'DESCRIPTOR': _LEVELBEHAVIOR,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.LevelBehavior)
})
_sym_db.RegisterMessage(LevelBehavior)
BehaviorGroup = _reflection.GeneratedProtocolMessageType('BehaviorGroup', (_message.Message,), {
'DESCRIPTOR': _BEHAVIORGROUP,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.BehaviorGroup)
})
_sym_db.RegisterMessage(BehaviorGroup)
BehaviorItem = _reflection.GeneratedProtocolMessageType('BehaviorItem', (_message.Message,), {
'DESCRIPTOR': _BEHAVIORITEM,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.BehaviorItem)
})
_sym_db.RegisterMessage(BehaviorItem)
Behavior = _reflection.GeneratedProtocolMessageType('Behavior', (_message.Message,), {
'DESCRIPTOR': _BEHAVIOR,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.Behavior)
})
_sym_db.RegisterMessage(Behavior)
Condition = _reflection.GeneratedProtocolMessageType('Condition', (_message.Message,), {
'DESCRIPTOR': _CONDITION,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.Condition)
})
_sym_db.RegisterMessage(Condition)
# @@protoc_insertion_point(module_scope)
| 51.169811 | 2,854 | 0.703386 | 3,756 | 32,544 | 5.743344 | 0.078541 | 0.050065 | 0.0916 | 0.077601 | 0.731087 | 0.659651 | 0.637725 | 0.615474 | 0.599759 | 0.599759 | 0 | 0.03926 | 0.191495 | 32,544 | 635 | 2,855 | 51.250394 | 0.780594 | 0.02111 | 0 | 0.68386 | 1 | 0.004992 | 0.146451 | 0.10647 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.003328 | 0.006656 | 0 | 0.006656 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0
| 2 |
d9c4481e6f2e6c4d81a9ed81d21838df61cf431f
| 26,272 |
py
|
Python
|
tests/keras/layers/wrappers_test.py
|
kalyc/keras-apache-mxnet
|
5497ebd50a45ccc446b8944ebbe11fb7721a5533
|
[
"MIT"
] | 300 |
2018-04-04T05:01:21.000Z
|
2022-02-25T18:56:04.000Z
|
tests/keras/layers/wrappers_test.py
|
kalyc/keras-apache-mxnet
|
5497ebd50a45ccc446b8944ebbe11fb7721a5533
|
[
"MIT"
] | 163 |
2018-04-03T17:41:22.000Z
|
2021-09-03T16:44:04.000Z
|
tests/keras/layers/wrappers_test.py
|
kalyc/keras-apache-mxnet
|
5497ebd50a45ccc446b8944ebbe11fb7721a5533
|
[
"MIT"
] | 72 |
2018-04-21T06:42:30.000Z
|
2021-12-26T06:02:42.000Z
|
import pytest
import numpy as np
import copy
from numpy.testing import assert_allclose
from keras.utils import CustomObjectScope
from keras.layers import wrappers, Input, Layer
from keras.layers import RNN
from keras import layers
from keras.models import Sequential, Model, model_from_json
from keras import backend as K
from keras.utils.generic_utils import object_list_uid, to_list
@pytest.mark.skipif(K.backend() == 'mxnet',
reason='MXNet backend does not support TimeDistributed and RNN yet')
def test_TimeDistributed():
# first, test with Dense layer
model = Sequential()
model.add(wrappers.TimeDistributed(layers.Dense(2), input_shape=(3, 4)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(np.random.random((10, 3, 4)), np.random.random((10, 3, 2)),
epochs=1,
batch_size=10)
# test config
model.get_config()
# test when specifying a batch_input_shape
test_input = np.random.random((1, 3, 4))
test_output = model.predict(test_input)
weights = model.layers[0].get_weights()
reference = Sequential()
reference.add(wrappers.TimeDistributed(layers.Dense(2),
batch_input_shape=(1, 3, 4)))
reference.add(layers.Activation('relu'))
reference.compile(optimizer='rmsprop', loss='mse')
reference.layers[0].set_weights(weights)
reference_output = reference.predict(test_input)
assert_allclose(test_output, reference_output, atol=1e-05)
# test with Embedding
model = Sequential()
model.add(wrappers.TimeDistributed(layers.Embedding(5, 6),
batch_input_shape=(10, 3, 4),
dtype='int32'))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(np.random.randint(5, size=(10, 3, 4), dtype='int32'),
np.random.random((10, 3, 4, 6)), epochs=1, batch_size=10)
# compare to not using batch_input_shape
test_input = np.random.randint(5, size=(10, 3, 4), dtype='int32')
test_output = model.predict(test_input)
weights = model.layers[0].get_weights()
reference = Sequential()
reference.add(wrappers.TimeDistributed(layers.Embedding(5, 6),
input_shape=(3, 4), dtype='int32'))
reference.compile(optimizer='rmsprop', loss='mse')
reference.layers[0].set_weights(weights)
reference_output = reference.predict(test_input)
assert_allclose(test_output, reference_output, atol=1e-05)
# test with Conv2D
model = Sequential()
model.add(wrappers.TimeDistributed(layers.Conv2D(5, (2, 2),
padding='same'),
input_shape=(2, 4, 4, 3)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
model.train_on_batch(np.random.random((1, 2, 4, 4, 3)),
np.random.random((1, 2, 4, 4, 5)))
model = model_from_json(model.to_json())
model.summary()
# test stacked layers
model = Sequential()
model.add(wrappers.TimeDistributed(layers.Dense(2), input_shape=(3, 4)))
model.add(wrappers.TimeDistributed(layers.Dense(3)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(np.random.random((10, 3, 4)), np.random.random((10, 3, 3)),
epochs=1, batch_size=10)
# test wrapping Sequential model
model = Sequential()
model.add(layers.Dense(3, input_dim=2))
outer_model = Sequential()
outer_model.add(wrappers.TimeDistributed(model, input_shape=(3, 2)))
outer_model.compile(optimizer='rmsprop', loss='mse')
outer_model.fit(np.random.random((10, 3, 2)), np.random.random((10, 3, 3)),
epochs=1, batch_size=10)
# test with functional API
x = Input(shape=(3, 2))
y = wrappers.TimeDistributed(model)(x)
outer_model = Model(x, y)
outer_model.compile(optimizer='rmsprop', loss='mse')
outer_model.fit(np.random.random((10, 3, 2)), np.random.random((10, 3, 3)),
epochs=1, batch_size=10)
# test with BatchNormalization
model = Sequential()
model.add(wrappers.TimeDistributed(
layers.BatchNormalization(center=True, scale=True),
name='bn', input_shape=(10, 2)))
model.compile(optimizer='rmsprop', loss='mse')
# Assert that mean and variance are 0 and 1.
td = model.layers[0]
assert np.array_equal(td.get_weights()[2], np.array([0, 0]))
assert np.array_equal(td.get_weights()[3], np.array([1, 1]))
# Train
model.train_on_batch(np.random.normal(loc=2, scale=2, size=(1, 10, 2)),
np.broadcast_to(np.array([0, 1]), (1, 10, 2)))
# Assert that mean and variance changed.
assert not np.array_equal(td.get_weights()[2], np.array([0, 0]))
assert not np.array_equal(td.get_weights()[3], np.array([1, 1]))
# Verify input_map has one mapping from inputs to reshaped inputs.
uid = object_list_uid(model.inputs)
assert len(td._input_map.keys()) == 1
assert uid in td._input_map
assert K.int_shape(td._input_map[uid]) == (None, 2)
@pytest.mark.skipif(K.backend() == 'mxnet',
reason='MXNet backend does not support TimeDistributed and RNN yet')
@pytest.mark.skipif((K.backend() == 'cntk'),
reason='Flaky with CNTK backend')
def test_TimeDistributed_learning_phase():
# test layers that need learning_phase to be set
np.random.seed(1234)
x = Input(shape=(3, 2))
y = wrappers.TimeDistributed(layers.Dropout(.999))(x, training=True)
model = Model(x, y)
y = model.predict(np.random.random((10, 3, 2)))
assert_allclose(np.mean(y), 0., atol=1e-1, rtol=1e-1)
@pytest.mark.skipif(K.backend() == 'mxnet',
reason='MXNet backend does not support TimeDistributed and RNN yet')
def test_TimeDistributed_trainable():
# test layers that need learning_phase to be set
x = Input(shape=(3, 2))
layer = wrappers.TimeDistributed(layers.BatchNormalization())
_ = layer(x)
assert len(layer.updates) == 2
assert len(layer.trainable_weights) == 2
layer.trainable = False
assert len(layer.updates) == 0
assert len(layer.trainable_weights) == 0
layer.trainable = True
assert len(layer.updates) == 2
assert len(layer.trainable_weights) == 2
@pytest.mark.skipif((K.backend() == 'cntk' or K.backend() == 'mxnet'),
reason='Unknown timestamps for RNN not supported in CNTK and MXNet.')
def test_TimeDistributed_with_masked_embedding_and_unspecified_shape():
# test with unspecified shape and Embeddings with mask_zero
model = Sequential()
model.add(wrappers.TimeDistributed(layers.Embedding(5, 6, mask_zero=True),
input_shape=(None, None)))
# the shape so far: (N, t_1, t_2, 6)
model.add(wrappers.TimeDistributed(layers.SimpleRNN(7, return_sequences=True)))
model.add(wrappers.TimeDistributed(layers.SimpleRNN(8, return_sequences=False)))
model.add(layers.SimpleRNN(1, return_sequences=False))
model.compile(optimizer='rmsprop', loss='mse')
model_input = np.random.randint(low=1, high=5, size=(10, 3, 4), dtype='int32')
for i in range(4):
model_input[i, i:, i:] = 0
model.fit(model_input,
np.random.random((10, 1)), epochs=1, batch_size=10)
mask_outputs = [model.layers[0].compute_mask(model.input)]
for layer in model.layers[1:]:
mask_outputs.append(layer.compute_mask(layer.input, mask_outputs[-1]))
func = K.function([model.input], mask_outputs[:-1])
mask_outputs_val = func([model_input])
ref_mask_val_0 = model_input > 0 # embedding layer
ref_mask_val_1 = ref_mask_val_0 # first RNN layer
ref_mask_val_2 = np.any(ref_mask_val_1, axis=-1) # second RNN layer
ref_mask_val = [ref_mask_val_0, ref_mask_val_1, ref_mask_val_2]
for i in range(3):
assert np.array_equal(mask_outputs_val[i], ref_mask_val[i])
assert mask_outputs[-1] is None # final layer
@pytest.mark.skipif(K.backend() == 'mxnet',
reason='MXNet backend does not support TimeDistributed and RNN yet')
def test_TimeDistributed_with_masking_layer():
# test with Masking layer
model = Sequential()
model.add(wrappers.TimeDistributed(layers.Masking(mask_value=0.,),
input_shape=(None, 4)))
model.add(wrappers.TimeDistributed(layers.Dense(5)))
model.compile(optimizer='rmsprop', loss='mse')
model_input = np.random.randint(low=1, high=5, size=(10, 3, 4))
for i in range(4):
model_input[i, i:, :] = 0.
model.compile(optimizer='rmsprop', loss='mse')
model.fit(model_input,
np.random.random((10, 3, 5)), epochs=1, batch_size=6)
mask_outputs = [model.layers[0].compute_mask(model.input)]
mask_outputs += [model.layers[1].compute_mask(model.layers[1].input,
mask_outputs[-1])]
func = K.function([model.input], mask_outputs)
mask_outputs_val = func([model_input])
assert np.array_equal(mask_outputs_val[0], np.any(model_input, axis=-1))
assert np.array_equal(mask_outputs_val[1], np.any(model_input, axis=-1))
def test_regularizers():
model = Sequential()
model.add(wrappers.TimeDistributed(
layers.Dense(2, kernel_regularizer='l1'), input_shape=(3, 4)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
assert len(model.layers[0].layer.losses) == 1
assert len(model.layers[0].losses) == 1
assert len(model.layers[0].get_losses_for(None)) == 1
assert len(model.losses) == 1
model = Sequential()
model.add(wrappers.TimeDistributed(
layers.Dense(2, activity_regularizer='l1'), input_shape=(3, 4)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
assert len(model.losses) == 1
def test_Bidirectional():
rnn = layers.SimpleRNN
samples = 2
dim = 2
timesteps = 2
output_dim = 2
dropout_rate = 0.2
for mode in ['sum', 'concat']:
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == 'concat' else output_dim
y = np.random.random((samples, target_dim))
# test with Sequential model
model = Sequential()
model.add(wrappers.Bidirectional(rnn(output_dim, dropout=dropout_rate,
recurrent_dropout=dropout_rate),
merge_mode=mode,
input_shape=(timesteps, dim)))
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1, batch_size=1)
# test config
model.get_config()
model = model_from_json(model.to_json())
model.summary()
# test stacked bidirectional layers
model = Sequential()
model.add(wrappers.Bidirectional(rnn(output_dim,
return_sequences=True),
merge_mode=mode,
input_shape=(timesteps, dim)))
model.add(wrappers.Bidirectional(rnn(output_dim), merge_mode=mode))
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1, batch_size=1)
# test with functional API
inputs = Input((timesteps, dim))
outputs = wrappers.Bidirectional(rnn(output_dim, dropout=dropout_rate,
recurrent_dropout=dropout_rate),
merge_mode=mode)(inputs)
model = Model(inputs, outputs)
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1, batch_size=1)
# Bidirectional and stateful
inputs = Input(batch_shape=(1, timesteps, dim))
outputs = wrappers.Bidirectional(rnn(output_dim, stateful=True),
merge_mode=mode)(inputs)
model = Model(inputs, outputs)
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1, batch_size=1)
@pytest.mark.skipif((K.backend() == 'cntk'),
reason='Unknown timestamps not supported in CNTK.')
def test_Bidirectional_dynamic_timesteps():
# test with functional API with dynamic length
rnn = layers.SimpleRNN
samples = 2
dim = 2
timesteps = 2
output_dim = 2
dropout_rate = 0.2
for mode in ['sum', 'concat']:
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == 'concat' else output_dim
y = np.random.random((samples, target_dim))
inputs = Input((None, dim))
outputs = wrappers.Bidirectional(rnn(output_dim, dropout=dropout_rate,
recurrent_dropout=dropout_rate),
merge_mode=mode)(inputs)
model = Model(inputs, outputs)
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1, batch_size=1)
@pytest.mark.parametrize('merge_mode', ['sum', 'mul', 'ave', 'concat', None])
def test_Bidirectional_merged_value(merge_mode):
rnn = layers.LSTM
samples = 2
dim = 5
timesteps = 3
units = 3
X = [np.random.rand(samples, timesteps, dim)]
if merge_mode == 'sum':
merge_func = lambda y, y_rev: y + y_rev
elif merge_mode == 'mul':
merge_func = lambda y, y_rev: y * y_rev
elif merge_mode == 'ave':
merge_func = lambda y, y_rev: (y + y_rev) / 2
elif merge_mode == 'concat':
merge_func = lambda y, y_rev: np.concatenate((y, y_rev), axis=-1)
else:
merge_func = lambda y, y_rev: [y, y_rev]
# basic case
inputs = Input((timesteps, dim))
layer = wrappers.Bidirectional(rnn(units, return_sequences=True),
merge_mode=merge_mode)
f_merged = K.function([inputs], to_list(layer(inputs)))
f_forward = K.function([inputs], [layer.forward_layer.call(inputs)])
f_backward = K.function([inputs],
[K.reverse(layer.backward_layer.call(inputs), 1)])
y_merged = f_merged(X)
y_expected = to_list(merge_func(f_forward(X)[0], f_backward(X)[0]))
assert len(y_merged) == len(y_expected)
for x1, x2 in zip(y_merged, y_expected):
assert_allclose(x1, x2, atol=1e-5)
# test return_state
inputs = Input((timesteps, dim))
layer = wrappers.Bidirectional(rnn(units, return_state=True),
merge_mode=merge_mode)
f_merged = K.function([inputs], layer(inputs))
f_forward = K.function([inputs], layer.forward_layer.call(inputs))
f_backward = K.function([inputs], layer.backward_layer.call(inputs))
n_states = len(layer.layer.states)
y_merged = f_merged(X)
y_forward = f_forward(X)
y_backward = f_backward(X)
y_expected = to_list(merge_func(y_forward[0], y_backward[0]))
assert len(y_merged) == len(y_expected) + n_states * 2
for x1, x2 in zip(y_merged, y_expected):
assert_allclose(x1, x2, atol=1e-5)
# test if the state of a BiRNN is the concatenation of the underlying RNNs
y_merged = y_merged[-n_states * 2:]
y_forward = y_forward[-n_states:]
y_backward = y_backward[-n_states:]
for state_birnn, state_inner in zip(y_merged, y_forward + y_backward):
assert_allclose(state_birnn, state_inner, atol=1e-5)
@pytest.mark.skipif(K.backend() == 'theano' or K.backend() == 'mxnet', reason='Not supported.')
@pytest.mark.parametrize('merge_mode', ['sum', 'concat', None])
def test_Bidirectional_dropout(merge_mode):
rnn = layers.LSTM
samples = 2
dim = 5
timesteps = 3
units = 3
X = [np.random.rand(samples, timesteps, dim)]
inputs = Input((timesteps, dim))
wrapped = wrappers.Bidirectional(rnn(units, dropout=0.2, recurrent_dropout=0.2),
merge_mode=merge_mode)
outputs = to_list(wrapped(inputs, training=True))
assert all(not getattr(x, '_uses_learning_phase') for x in outputs)
inputs = Input((timesteps, dim))
wrapped = wrappers.Bidirectional(rnn(units, dropout=0.2, return_state=True),
merge_mode=merge_mode)
outputs = to_list(wrapped(inputs))
assert all(x._uses_learning_phase for x in outputs)
model = Model(inputs, outputs)
assert model.uses_learning_phase
y1 = to_list(model.predict(X))
y2 = to_list(model.predict(X))
for x1, x2 in zip(y1, y2):
assert_allclose(x1, x2, atol=1e-5)
def test_Bidirectional_state_reuse():
rnn = layers.LSTM
samples = 2
dim = 5
timesteps = 3
units = 3
input1 = Input((timesteps, dim))
layer = wrappers.Bidirectional(rnn(units, return_state=True,
return_sequences=True))
state = layer(input1)[1:]
# test passing invalid initial_state: passing a tensor
input2 = Input((timesteps, dim))
with pytest.raises(ValueError):
output = wrappers.Bidirectional(rnn(units))(input2, initial_state=state[0])
# test valid usage: passing a list
output = wrappers.Bidirectional(rnn(units))(input2, initial_state=state)
model = Model([input1, input2], output)
assert len(model.layers) == 4
assert isinstance(model.layers[-1].input, list)
inputs = [np.random.rand(samples, timesteps, dim),
np.random.rand(samples, timesteps, dim)]
outputs = model.predict(inputs)
@pytest.mark.skipif(K.backend() == 'mxnet',
reason='MXNet backend does not support custom RNN cell yet')
def test_Bidirectional_with_constants():
class RNNCellWithConstants(Layer):
def __init__(self, units, **kwargs):
self.units = units
self.state_size = units
super(RNNCellWithConstants, self).__init__(**kwargs)
def build(self, input_shape):
if not isinstance(input_shape, list):
raise TypeError('expects constants shape')
[input_shape, constant_shape] = input_shape
# will (and should) raise if more than one constant passed
self.input_kernel = self.add_weight(
shape=(input_shape[-1], self.units),
initializer='uniform',
name='kernel')
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
initializer='uniform',
name='recurrent_kernel')
self.constant_kernel = self.add_weight(
shape=(constant_shape[-1], self.units),
initializer='uniform',
name='constant_kernel')
self.built = True
def call(self, inputs, states, constants):
[prev_output] = states
[constant] = constants
h_input = K.dot(inputs, self.input_kernel)
h_state = K.dot(prev_output, self.recurrent_kernel)
h_const = K.dot(constant, self.constant_kernel)
output = h_input + h_state + h_const
return output, [output]
def get_config(self):
config = {'units': self.units}
base_config = super(RNNCellWithConstants, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# Test basic case.
x = Input((5, 5))
c = Input((3,))
cell = RNNCellWithConstants(32)
custom_objects = {'RNNCellWithConstants': RNNCellWithConstants}
with CustomObjectScope(custom_objects):
layer = wrappers.Bidirectional(RNN(cell))
y = layer(x, constants=c)
model = Model([x, c], y)
model.compile(optimizer='rmsprop', loss='mse')
model.train_on_batch(
[np.zeros((6, 5, 5)), np.zeros((6, 3))],
np.zeros((6, 64))
)
# Test basic case serialization.
x_np = np.random.random((6, 5, 5))
c_np = np.random.random((6, 3))
y_np = model.predict([x_np, c_np])
weights = model.get_weights()
config = layer.get_config()
with CustomObjectScope(custom_objects):
layer = wrappers.Bidirectional.from_config(copy.deepcopy(config))
y = layer(x, constants=c)
model = Model([x, c], y)
model.set_weights(weights)
y_np_2 = model.predict([x_np, c_np])
assert_allclose(y_np, y_np_2, atol=1e-4)
# test flat list inputs
with CustomObjectScope(custom_objects):
layer = wrappers.Bidirectional.from_config(copy.deepcopy(config))
y = layer([x, c])
model = Model([x, c], y)
model.set_weights(weights)
y_np_3 = model.predict([x_np, c_np])
assert_allclose(y_np, y_np_3, atol=1e-4)
@pytest.mark.skipif(K.backend() == 'mxnet',
reason='MXNet backend does not support custom RNN cell yet')
def test_Bidirectional_with_constants_layer_passing_initial_state():
class RNNCellWithConstants(Layer):
def __init__(self, units, **kwargs):
self.units = units
self.state_size = units
super(RNNCellWithConstants, self).__init__(**kwargs)
def build(self, input_shape):
if not isinstance(input_shape, list):
raise TypeError('expects constants shape')
[input_shape, constant_shape] = input_shape
# will (and should) raise if more than one constant passed
self.input_kernel = self.add_weight(
shape=(input_shape[-1], self.units),
initializer='uniform',
name='kernel')
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
initializer='uniform',
name='recurrent_kernel')
self.constant_kernel = self.add_weight(
shape=(constant_shape[-1], self.units),
initializer='uniform',
name='constant_kernel')
self.built = True
def call(self, inputs, states, constants):
[prev_output] = states
[constant] = constants
h_input = K.dot(inputs, self.input_kernel)
h_state = K.dot(prev_output, self.recurrent_kernel)
h_const = K.dot(constant, self.constant_kernel)
output = h_input + h_state + h_const
return output, [output]
def get_config(self):
config = {'units': self.units}
base_config = super(RNNCellWithConstants, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# Test basic case.
x = Input((5, 5))
c = Input((3,))
s_for = Input((32,))
s_bac = Input((32,))
cell = RNNCellWithConstants(32)
custom_objects = {'RNNCellWithConstants': RNNCellWithConstants}
with CustomObjectScope(custom_objects):
layer = wrappers.Bidirectional(RNN(cell))
y = layer(x, initial_state=[s_for, s_bac], constants=c)
model = Model([x, s_for, s_bac, c], y)
model.compile(optimizer='rmsprop', loss='mse')
model.train_on_batch(
[np.zeros((6, 5, 5)), np.zeros((6, 32)),
np.zeros((6, 32)), np.zeros((6, 3))],
np.zeros((6, 64))
)
# Test basic case serialization.
x_np = np.random.random((6, 5, 5))
s_fw_np = np.random.random((6, 32))
s_bk_np = np.random.random((6, 32))
c_np = np.random.random((6, 3))
y_np = model.predict([x_np, s_fw_np, s_bk_np, c_np])
weights = model.get_weights()
config = layer.get_config()
with CustomObjectScope(custom_objects):
layer = wrappers.Bidirectional.from_config(copy.deepcopy(config))
y = layer(x, initial_state=[s_for, s_bac], constants=c)
model = Model([x, s_for, s_bac, c], y)
model.set_weights(weights)
y_np_2 = model.predict([x_np, s_fw_np, s_bk_np, c_np])
assert_allclose(y_np, y_np_2, atol=1e-4)
# verify that state is used
y_np_2_different_s = model.predict([x_np, s_fw_np + 10., s_bk_np + 10., c_np])
with pytest.raises(AssertionError):
assert_allclose(y_np, y_np_2_different_s, atol=1e-4)
# test flat list inputs
with CustomObjectScope(custom_objects):
layer = wrappers.Bidirectional.from_config(copy.deepcopy(config))
y = layer([x, s_for, s_bac, c])
model = Model([x, s_for, s_bac, c], y)
model.set_weights(weights)
y_np_3 = model.predict([x_np, s_fw_np, s_bk_np, c_np])
assert_allclose(y_np, y_np_3, atol=1e-4)
def test_Bidirectional_trainable():
# test layers that need learning_phase to be set
x = Input(shape=(3, 2))
layer = wrappers.Bidirectional(layers.SimpleRNN(3))
_ = layer(x)
assert len(layer.trainable_weights) == 6
layer.trainable = False
assert len(layer.trainable_weights) == 0
layer.trainable = True
assert len(layer.trainable_weights) == 6
def test_Bidirectional_updates():
x = Input(shape=(3, 2))
layer = wrappers.Bidirectional(layers.SimpleRNN(3))
assert len(layer.updates) == 0
assert len(layer.get_updates_for(None)) == 0
assert len(layer.get_updates_for(x)) == 0
layer.forward_layer.add_update(0, inputs=x)
layer.forward_layer.add_update(1, inputs=None)
layer.backward_layer.add_update(0, inputs=x)
layer.backward_layer.add_update(1, inputs=None)
assert len(layer.updates) == 4
assert len(layer.get_updates_for(None)) == 2
assert len(layer.get_updates_for(x)) == 2
def test_Bidirectional_losses():
x = Input(shape=(3, 2))
layer = wrappers.Bidirectional(
layers.SimpleRNN(3, kernel_regularizer='l1', bias_regularizer='l1'))
_ = layer(x)
assert len(layer.losses) == 4
assert len(layer.get_losses_for(None)) == 4
assert len(layer.get_losses_for(x)) == 0
layer.forward_layer.add_loss(0, inputs=x)
layer.forward_layer.add_loss(1, inputs=None)
layer.backward_layer.add_loss(0, inputs=x)
layer.backward_layer.add_loss(1, inputs=None)
assert len(layer.losses) == 8
assert len(layer.get_losses_for(None)) == 6
assert len(layer.get_losses_for(x)) == 2
if __name__ == '__main__':
pytest.main([__file__])
| 40.418462 | 95 | 0.629225 | 3,476 | 26,272 | 4.577675 | 0.086594 | 0.017597 | 0.021996 | 0.027149 | 0.794558 | 0.760872 | 0.714178 | 0.637192 | 0.602061 | 0.562909 | 0 | 0.023567 | 0.242502 | 26,272 | 649 | 96 | 40.48074 | 0.775991 | 0.052223 | 0 | 0.58365 | 0 | 0 | 0.044619 | 0 | 0 | 0 | 0 | 0 | 0.110266 | 1 | 0.045627 | false | 0.001901 | 0.020913 | 0 | 0.077947 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0
| 2 |
d9e48585d735333916bf5e8b10a68c72e4541093
| 248,866 |
py
|
Python
|
pysnmp-with-texts/XXX-MIB.py
|
agustinhenze/mibs.snmplabs.com
|
1fc5c07860542b89212f4c8ab807057d9a9206c7
|
[
"Apache-2.0"
] | 8 |
2019-05-09T17:04:00.000Z
|
2021-06-09T06:50:51.000Z
|
pysnmp-with-texts/XXX-MIB.py
|
agustinhenze/mibs.snmplabs.com
|
1fc5c07860542b89212f4c8ab807057d9a9206c7
|
[
"Apache-2.0"
] | 4 |
2019-05-31T16:42:59.000Z
|
2020-01-31T21:57:17.000Z
|
pysnmp-with-texts/XXX-MIB.py
|
agustinhenze/mibs.snmplabs.com
|
1fc5c07860542b89212f4c8ab807057d9a9206c7
|
[
"Apache-2.0"
] | 10 |
2019-04-30T05:51:36.000Z
|
2022-02-16T03:33:41.000Z
| "#\n# PySNMP MIB module XXX-MIB (http://snmplabs.com/pysmi)\n# ASN.1 source file:///Users/davwang4/D(...TRUNCATED) | 141.884835 | 10,350 | 0.750826 | 35,253 | 248,866 | 5.18007 | 0.032933 | 0.019035 | 0.128912 | 0.013427 | 0.785557 | 0.679399 | 0.599393 | 0.548658 | 0.529563 | 0.47282 | 0 | 0.093998 | 0.084889 | 248,866 | 1,753 | 10,351 | 141.965773 | 0.707851 | 0.001238 | 0 | 0 | 0 | 0.014327 | 0.195744 | 0.018616 | 0 | 0 | 0.000322 | 0 | 0 | 1 | 0 | false | 0.001146 | 0.003438 | 0 | 0.003438 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0
| 2 |
d9e78859b4482aaef1db18210493138799d91b2f
| 1,969 |
py
|
Python
|
MIDI Remote Scripts/Push2/mode_collector.py
|
aarkwright/ableton_devices
|
fe5df3bbd64ccbc136bba722ba1e131a02969798
|
[
"MIT"
] | null | null | null |
MIDI Remote Scripts/Push2/mode_collector.py
|
aarkwright/ableton_devices
|
fe5df3bbd64ccbc136bba722ba1e131a02969798
|
[
"MIT"
] | null | null | null |
MIDI Remote Scripts/Push2/mode_collector.py
|
aarkwright/ableton_devices
|
fe5df3bbd64ccbc136bba722ba1e131a02969798
|
[
"MIT"
] | null | null | null | "# uncompyle6 version 3.3.5\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.7.3 (default(...TRUNCATED) | 37.865385 | 124 | 0.742509 | 278 | 1,969 | 4.827338 | 0.298561 | 0.059613 | 0.052161 | 0.056632 | 0.42623 | 0.271237 | 0.201192 | 0.153502 | 0.153502 | 0.153502 | 0 | 0.031882 | 0.171661 | 1,969 | 52 | 125 | 37.865385 | 0.790926 | 0.151854 | 0 | 0.216216 | 0 | 0 | 0.031231 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.243243 | false | 0 | 0.054054 | 0.108108 | 0.432432 | 0.027027 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
0
| 2 |
d9e7a46d631c672aae25d04f18b75876427b787e
| 817 |
py
|
Python
|
src/topicModel.py
|
daidaotong/SingleView
|
db3249ca5afba97f750495cccbc185de88bf2287
|
[
"MIT"
] | null | null | null |
src/topicModel.py
|
daidaotong/SingleView
|
db3249ca5afba97f750495cccbc185de88bf2287
|
[
"MIT"
] | null | null | null |
src/topicModel.py
|
daidaotong/SingleView
|
db3249ca5afba97f750495cccbc185de88bf2287
|
[
"MIT"
] | null | null | null | "from gensim import corpora, models, similarities, matutils,utils\nfrom gensim.models import KeyedVe(...TRUNCATED) | 35.521739 | 111 | 0.71481 | 106 | 817 | 5.349057 | 0.528302 | 0.116402 | 0.112875 | 0.10582 | 0.185185 | 0.185185 | 0 | 0 | 0 | 0 | 0 | 0.023591 | 0.066095 | 817 | 22 | 112 | 37.136364 | 0.719528 | 0.051408 | 0 | 0 | 0 | 0 | 0.226766 | 0.078067 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.428571 | null | null | 0.285714 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
0
| 2 |
d9eb0ee449a6b916e969b15c42a07550484f36ad
| 959 |
py
|
Python
|
djangocms_baseplugins/spacer/cms_plugins.py
|
benzkji/djangocms-baseplugins
|
7f041a030ed93dcdec70e4ca777b841846b8f2f2
|
[
"MIT"
] | 2 |
2019-04-14T01:31:22.000Z
|
2020-03-05T13:06:57.000Z
|
djangocms_baseplugins/spacer/cms_plugins.py
|
benzkji/djangocms-baseplugins
|
7f041a030ed93dcdec70e4ca777b841846b8f2f2
|
[
"MIT"
] | 32 |
2017-04-04T09:28:06.000Z
|
2021-08-18T16:23:02.000Z
|
djangocms_baseplugins/spacer/cms_plugins.py
|
bnzk/djangocms-baseplugins
|
7f041a030ed93dcdec70e4ca777b841846b8f2f2
|
[
"MIT"
] | null | null | null | "# coding: utf-8\nfrom cms.plugin_base import CMSPluginBase\nfrom cms.plugin_pool import plugin_pool(...TRUNCATED) | 29.96875 | 100 | 0.788321 | 112 | 959 | 6.526786 | 0.419643 | 0.109439 | 0.098495 | 0.139535 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001233 | 0.154327 | 959 | 31 | 101 | 30.935484 | 0.900123 | 0.027112 | 0 | 0.090909 | 0 | 0 | 0.041935 | 0.035484 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.409091 | 0 | 0.818182 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
0
| 2 |
d9f9595b5ef66170be57096ea8261b3da13883ac
| 132 |
py
|
Python
|
functional_tests.py
|
gustavomazevedo/tbackup-client
|
eb2fdf75eff7abf17c9bce12920de793ba760f61
|
[
"MIT"
] | null | null | null |
functional_tests.py
|
gustavomazevedo/tbackup-client
|
eb2fdf75eff7abf17c9bce12920de793ba760f61
|
[
"MIT"
] | null | null | null |
functional_tests.py
|
gustavomazevedo/tbackup-client
|
eb2fdf75eff7abf17c9bce12920de793ba760f61
|
[
"MIT"
] | null | null | null | "from selenium import webdriver\n\nbrowser = webdriver.Firefox()\nbrowser.get('http://localhost:8000(...TRUNCATED) | 22 | 36 | 0.780303 | 17 | 132 | 6.058824 | 0.823529 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.033613 | 0.098485 | 132 | 6 | 37 | 22 | 0.831933 | 0 | 0 | 0 | 0 | 0 | 0.203008 | 0 | 0 | 0 | 0 | 0 | 0.25 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0
| 2 |
d9ff0e5cd63921d7a1a7f3f682d268671ab38688
| 834 |
py
|
Python
|
main.py
|
Yash-s-Code-Camp/Python-Day-4
|
887c4e172905b2b0dea493a3c9c1f61e403556fc
|
[
"MIT"
] | null | null | null |
main.py
|
Yash-s-Code-Camp/Python-Day-4
|
887c4e172905b2b0dea493a3c9c1f61e403556fc
|
[
"MIT"
] | null | null | null |
main.py
|
Yash-s-Code-Camp/Python-Day-4
|
887c4e172905b2b0dea493a3c9c1f61e403556fc
|
[
"MIT"
] | null | null | null | "# def mul(a):\n# \treturn lambda b:b*a\n\n# singler = mul(1) # addition = lambda b:b*1\n# double(...TRUNCATED) | 17.020408 | 47 | 0.603118 | 128 | 834 | 3.804688 | 0.34375 | 0.057495 | 0.065708 | 0.098563 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.089368 | 0.221823 | 834 | 48 | 48 | 17.375 | 0.661017 | 0.423261 | 0 | 0 | 0 | 0 | 0.111597 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0 | 0 | 0.111111 | 0.444444 | 0.111111 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
0
| 2 |
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