text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|>avetxt("valid.csv", valid, fmt='%s', delimiter=',', header= head)
np.savetxt("test.csv", test, fmt='%s', delimiter=',', header= head)
filename = sys.argv[1]
createtv(filename)<|fim_prefix|># repo: nandini269/graph-hyperband path: /create_tv.py
import sys
import numpy as np
import csv
def createtv(fil... | code_fim | medium | {
"lang": "python",
"repo": "nandini269/graph-hyperband",
"path": "/create_tv.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nandini269/graph-hyperband path: /create_tv.py
import sys
import numpy as np
import csv
def createtv(filename):
r = csv.reader(open(filename), delimiter=",")
res = np.array(list(r))
head = res[0][0]
for i in range(1,len(res[0])):
head = head + ',' + res[0][i]
#print(head)
res = res[1:]... | code_fim | medium | {
"lang": "python",
"repo": "nandini269/graph-hyperband",
"path": "/create_tv.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yanchdh/LeetCode path: /80-89/88_Merge Sorted Array.py
# -*- coding:utf-8 -*-
# https://leetcode.com/problems/merge-sorted-array/description/
class Solution(object):
<|fim_suffix|> """
:type nums1: List[int]
:type m: int
:type nums2: List[int]
:type n: int
... | code_fim | hard | {
"lang": "python",
"repo": "yanchdh/LeetCode",
"path": "/80-89/88_Merge Sorted Array.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.tmp_dir = tempfile.mkdtemp()
self.workspace = context.Workspace(self.tmp_dir)
def tearDown(self):
shutil.rmtree(self.tmp_dir)
def test_contains(self):
p = 'foo'
self.assertFalse(self.workspace.Contains(p))
with open(os.path.join(self.tmp_dir, ... | code_fim | medium | {
"lang": "python",
"repo": "GoogleCloudPlatform/runtimes-common",
"path": "/ftl/common/context_test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> shutil.rmtree(self.tmp_dir)
def test_contains(self):
p = 'foo'
self.assertFalse(self.workspace.Contains(p))
with open(os.path.join(self.tmp_dir, p), 'w') as f:
f.write('hey')
self.assertTrue(self.workspace.Contains(p))
# Subdir
d = ... | code_fim | medium | {
"lang": "python",
"repo": "GoogleCloudPlatform/runtimes-common",
"path": "/ftl/common/context_test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: GoogleCloudPlatform/runtimes-common path: /ftl/common/context_test.py
# Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
... | code_fim | medium | {
"lang": "python",
"repo": "GoogleCloudPlatform/runtimes-common",
"path": "/ftl/common/context_test.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def main():
L = [2, 4, 6, 2, 5]
print(f"largest sum of non-adjacent numbers of L={L} -> {largest_sum_nonadjacents_numbers_1(L)}")
L = [5, 1, 1, 5]
print(f"largest sum of non-adjacent numbers of L={L} -> {largest_sum_nonadjacents_numbers_1(L)}")
if __name__ == '__main__':
main()<|fim... | code_fim | hard | {
"lang": "python",
"repo": "yoyonel/DailyCodingProblem",
"path": "/src/dailycodingproblem/9_Sum_Of_Non_Adjacent_Numbers/app.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yoyonel/DailyCodingProblem path: /src/dailycodingproblem/9_Sum_Of_Non_Adjacent_Numbers/app.py
"""
largest sum of non-adjacent numbers of L=[2, 4, 6, 2, 5] -> 13
largest sum of non-adjacent numbers of L=[5, 1, 1, 5] -> 10
"""
from typing import List
<|fim_suffix|>
def largest_sum_nonadjacents_num... | code_fim | hard | {
"lang": "python",
"repo": "yoyonel/DailyCodingProblem",
"path": "/src/dailycodingproblem/9_Sum_Of_Non_Adjacent_Numbers/app.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: muhaiminmuh/School-Academic-Management-System-SIAKAD path: /master/admin.py
from django.contrib import admin
from master.models import *
# Register your models here.
class ProgramStudiAdmin (admin.ModelAdmin) :
list_display = ['kode_progdi', 'nama_progdi']
list_filter = ()
search_fields = ['... | code_fim | hard | {
"lang": "python",
"repo": "muhaiminmuh/School-Academic-Management-System-SIAKAD",
"path": "/master/admin.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>admin.site.register(MataKuliah, MataKuliahAdmin)
class KelasAdmin (admin.ModelAdmin) :
list_display = ['nama_kelas']
search_fields = ['nama_kelas']
list_per_page = 20
admin.site.register(Kelas, KelasAdmin)
class KurikulumAdmin (admin.ModelAdmin ) :
list_display = ['kode_kurikulum', 'nama_kurikulum'... | code_fim | hard | {
"lang": "python",
"repo": "muhaiminmuh/School-Academic-Management-System-SIAKAD",
"path": "/master/admin.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>#make boxplot
sns.set_style("whitegrid")
b=sns.color_palette(["#866080"])
box_plot=sns.boxplot(y='Dice', x='Algorithm', data=df, palette=b)
plt.xlabel('Skull-Stripping Methods')
plt.ylabel('Dice Similarity Coefficients')
plt.ylim((0.7,1.0))
plt.title('NFBS')
plt.savefig('boxplot_NFBS2.png')<|fim_prefix|>#... | code_fim | hard | {
"lang": "python",
"repo": "preprocessed-connectomes-project/NFB_skullstripped",
"path": "/validation_scripts/dice_boxplot_NFBS.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: preprocessed-connectomes-project/NFB_skullstripped path: /validation_scripts/dice_boxplot_NFBS.py
#DICE coefficient plot
#Ben Puccio
#2016-06-08
#
#
#Load numpy arrays of NFBS dice coefficients from dice.py
#Make boxplot using Matplotlib and Seaborn
import numpy as np
import matplotlib.pyplot a... | code_fim | hard | {
"lang": "python",
"repo": "preprocessed-connectomes-project/NFB_skullstripped",
"path": "/validation_scripts/dice_boxplot_NFBS.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>total_elements = len(cc_imgs)
batch_size = 32
cc_data_de = {"images":[], "dataset": cc_data['dataset']}
output_cc_data_path = '/'.join([data_path, "dataset_cc_de.json"])
start_time = time.perf_counter()
for i in tqdm(range(0,total_elements,batch_size)):
#form the batch of sentences
captions_en_ba... | code_fim | hard | {
"lang": "python",
"repo": "zmykevin/fairseq",
"path": "/translate_cc.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>#Translate the CC
cc_imgs = cc_data['images']
total_elements = len(cc_imgs)
batch_size = 32
cc_data_de = {"images":[], "dataset": cc_data['dataset']}
output_cc_data_path = '/'.join([data_path, "dataset_cc_de.json"])
start_time = time.perf_counter()
for i in tqdm(range(0,total_elements,batch_size)):
... | code_fim | medium | {
"lang": "python",
"repo": "zmykevin/fairseq",
"path": "/translate_cc.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zmykevin/fairseq path: /translate_cc.py
import torch
import os
import json
from tqdm import tqdm
import time
#Load the translation model
#en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt16.en-de',tokenizer='moses', bpe='subword_nmt')
en2de = torch.hub.load('pytorch/fairseq', 'transform... | code_fim | hard | {
"lang": "python",
"repo": "zmykevin/fairseq",
"path": "/translate_cc.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for next_piece in pieces:
planner.game.piece = piece
planner.game.next_piece = next_piece
key1 = (letter_pieces[piece], letter_pieces[next_piece])
if not key1 in self[key0]:
move = planner.move()
letter = letter_pieces[move.piece]
rotation = m... | code_fim | medium | {
"lang": "python",
"repo": "vancezuo/block-battle-bot",
"path": "/openings.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vancezuo/block-battle-bot path: /openings.py
#!/usr/bin/env python
from __future__ import print_function, division
from game import Game, Placement, pieces, letter_pieces, piece_letters
from collections import deque
from copy import copy
<|fim_suffix|> key1 = (letter_pieces[piece], ... | code_fim | hard | {
"lang": "python",
"repo": "vancezuo/block-battle-bot",
"path": "/openings.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AsadRasheed-AR/Xtreme-Vision path: /xtreme_vision/Segmentation/cdcl/inference_15parts_skeletons.py
ori_paf_idx = [12, 13, 20, 21, 14, 15, 16, 17, 22, 23, 24, 25, 0, 1, 2, 3, \
4, 5, 6, 7, 8, 9, 10, 11, 28, 29, 30, 31, 34,35, 32, 33, 36, 37, 18, 19, 26, 27]
flip_paf_idx = [20, 21... | code_fim | hard | {
"lang": "python",
"repo": "AsadRasheed-AR/Xtreme-Vision",
"path": "/xtreme_vision/Segmentation/cdcl/inference_15parts_skeletons.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> connection_all.append(connection)
else:
special_k.append(k)
connection_all.append([])
# last number in each row is the total parts number of that person
# the second last number in each row is the score of the overall configuration
subset = -1 * np.... | code_fim | hard | {
"lang": "python",
"repo": "AsadRasheed-AR/Xtreme-Vision",
"path": "/xtreme_vision/Segmentation/cdcl/inference_15parts_skeletons.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> segmap_a = np.maximum(segmap_scale1,segmap_scale2)
segmap_b = np.maximum(segmap_scale4,segmap_scale3)
segmap_c = np.maximum(segmap_scale5,segmap_scale6)
segmap_d = np.maximum(segmap_scale7,segmap_scale8)
seg_ori = np.maximum(segmap_a, segmap_b)
seg_flip = np.maximum(segmap_c, segma... | code_fim | hard | {
"lang": "python",
"repo": "AsadRasheed-AR/Xtreme-Vision",
"path": "/xtreme_vision/Segmentation/cdcl/inference_15parts_skeletons.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alpinho/winrepo path: /profiles/views.py
matching_domains = list(
Q(domains__contains=code)
for code, name in Profile.get_domains_choices()
if st_regex.match(name)
)
st_conditions = [
... | code_fim | hard | {
"lang": "python",
"repo": "alpinho/winrepo",
"path": "/profiles/views.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class UserPasswordResetView(FormView):
form_class = PasswordResetForm
template_name = 'registration/reset_password.html'
success_message = 'If your e-mail address is in our registry, you will receive an e-mail soon on how to reset your password.'
def get(self, request, *args, **kwargs):
... | code_fim | hard | {
"lang": "python",
"repo": "alpinho/winrepo",
"path": "/profiles/views.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # deactivate account until email is verified
self.object = original_user
form.instance.is_active = False
form.instance.email = original_user.email
self.success_message = self.email_success_message
form.changed... | code_fim | hard | {
"lang": "python",
"repo": "alpinho/winrepo",
"path": "/profiles/views.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: SJTU-Thinklab-Det/DOTA-DOAI path: /FPN_Tensorflow/libs/networks/resnet.py
# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, division
import tensorflow as tf
import tensorflow.contrib.slim as slim
from libs.configs import cfgs
from tensorflow.contrib.slim.nets impo... | code_fim | hard | {
"lang": "python",
"repo": "SJTU-Thinklab-Det/DOTA-DOAI",
"path": "/FPN_Tensorflow/libs/networks/resnet.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def resnet_base(img_batch, scope_name, is_training=True):
'''
this code is derived from light-head rcnn.
https://github.com/zengarden/light_head_rcnn
It is convenient to freeze blocks. So we adapt this mode.
'''
if scope_name == 'resnet_v1_50':
middle_num_units = 6
eli... | code_fim | hard | {
"lang": "python",
"repo": "SJTU-Thinklab-Det/DOTA-DOAI",
"path": "/FPN_Tensorflow/libs/networks/resnet.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def ajax_insert_absent(request, coursid, etudiantid):
if True:#request.is_ajax():
cours = Cours.objects.get(id=coursid)
user = User.objects.get(username=etudiantid)
etudiant = Etudiant.objects.get(user=user)
absence = Absence(cours = cours, etudiant = etudiant)
... | code_fim | hard | {
"lang": "python",
"repo": "tornoz/ezvezans",
"path": "/absences/abs/views.py",
"mode": "spm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tornoz/ezvezans path: /absences/abs/views.py
from django.shortcuts import render, redirect
from django.http import HttpResponse
from django.contrib.auth.decorators import login_required
from django.template import RequestContext, loader
from django.contrib.auth.models import User, Group
from abs.... | code_fim | hard | {
"lang": "python",
"repo": "tornoz/ezvezans",
"path": "/absences/abs/views.py",
"mode": "psm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_suffix|> #Formset
Formset = modelformset_factory(Absence)
var['formset'] = Formset(queryset=Absence.objects.none())
#Limite les cours du formset à ceux de l'enseignant
var['formset'].forms[0].fields['cours'].queryset = Cours.objects.filter(enseignant = var['enseigna... | code_fim | hard | {
"lang": "python",
"repo": "tornoz/ezvezans",
"path": "/absences/abs/views.py",
"mode": "spm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tillhainbach/pyansiescapes path: /tests/test_color_256.py
from pyansiescapes.pyansiescapes import ColorDrawingLevel, Colors, Colors256
from itertools import chain
from collections.abc import Iterable
import sys
def _print_color(color = "0", colormode = "", color256 = "", drawing_level = "3", dis... | code_fim | hard | {
"lang": "python",
"repo": "tillhainbach/pyansiescapes",
"path": "/tests/test_color_256.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if not isinstance(drawing_level, ColorDrawingLevel):
drawing_level = ColorDrawingLevel[drawing_level]
if any((display_hex, display_rgb, display_hsl)):
# just display 256 bit colors
colormodes = [256]
display_string_length = 6
max_characters_per_line = 72
iter_... | code_fim | hard | {
"lang": "python",
"repo": "tillhainbach/pyansiescapes",
"path": "/tests/test_color_256.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for colormode in colormodes:
colormode_string = ""
color256 = ""
for color in chain.from_iterable(iter_dict[colormode]):
display_name = _make_display_name(color, display_colorid, display_colorname, display_hex, display_rgb, display_hsl)
color_int = int(c... | code_fim | hard | {
"lang": "python",
"repo": "tillhainbach/pyansiescapes",
"path": "/tests/test_color_256.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: JohnReid/bioinf-utilities path: /scripts/seq-head
#!/usr/bin/env python2
#
# Copyright John Reid 2009, 2010, 2013
#
"""
Code that reads in sequences and outputs first so many
"""
<|fim_suffix|>for i, seq in zip(
xrange(options.num_seqs),
F.iterseq(input, corebio.seq.dna_alphabet... | code_fim | hard | {
"lang": "python",
"repo": "JohnReid/bioinf-utilities",
"path": "/scripts/seq-head",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>for i, seq in zip(
xrange(options.num_seqs),
F.iterseq(input, corebio.seq.dna_alphabet)):
F.writeseq(sys.stdout, seq)<|fim_prefix|># repo: JohnReid/bioinf-utilities path: /scripts/seq-head
#!/usr/bin/env python2
#
# Copyright John Reid 2009, 2010, 2013
#
"""
Code that reads in sequen... | code_fim | hard | {
"lang": "python",
"repo": "JohnReid/bioinf-utilities",
"path": "/scripts/seq-head",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>#
# Check args
#
if 1 != len(args):
print >> sys.stderr, 'USAGE: %s <fasta file>' % __file__
sys.exit(-1)
fasta = args[0]
if '-' == fasta:
input = sys.stdin
else:
input = bioinfutils.open_input(fasta)
for i, seq in zip(
xrange(options.num_seqs),
F.iterseq(input, corebio.se... | code_fim | hard | {
"lang": "python",
"repo": "JohnReid/bioinf-utilities",
"path": "/scripts/seq-head",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: daniel-andersen/Interactive-Tabletop-Projected-Old path: /Server/src/board/markers/marker_util.py
from default_marker import DefaultMarker
from triangle_marker import TriangleMarker
def create_marker_from_name(name=None, marker_id=-1):
if name is None:
return DefaultMarker(marker_id... | code_fim | hard | {
"lang": "python",
"repo": "daniel-andersen/Interactive-Tabletop-Projected-Old",
"path": "/Server/src/board/markers/marker_util.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> :param marker_result_list: Marker result list
:return: List of dictionaries with "contour" key filtered out
"""
return [filter_out_contour_from_marker_result(marker_result) for marker_result in marker_result_list]
def filter_out_contour_from_marker_result(marker_result):
"""
Filt... | code_fim | medium | {
"lang": "python",
"repo": "daniel-andersen/Interactive-Tabletop-Projected-Old",
"path": "/Server/src/board/markers/marker_util.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># get lemmatization
response = requests.get(
url='http://localhost:9001/get-lemma',
params={
'text': text,
},
)
print(response.json())
# get state abbrevitaions
response = requests.get(
url='http://localhost:9001/convert-state-abbreviation',
params={
'queries': json.du... | code_fim | hard | {
"lang": "python",
"repo": "ophirgal/sm-scraper",
"path": "/src/nlp/example.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ophirgal/sm-scraper path: /src/nlp/example.py
import requests, json
text = 'Police say John Doe was seen doing a thing in Elizabeth, New Jersey on May 32, 1999.'
print(f'INPUT: {text}')
<|fim_suffix|># get entities
response = requests.get(
url='http://localhost:9001/get-entities',
para... | code_fim | hard | {
"lang": "python",
"repo": "ophirgal/sm-scraper",
"path": "/src/nlp/example.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: initzhang/Hetu path: /examples/ctr/tf_models/tf_dcn_criteo.py
import tensorflow as tf
def cross_layer(x0, x1, device):
# x0: input embedding feature (batch_size, 26 * embedding_size + 13)
# x1: the output of last layer (batch_size, 26 * embedding_size + 13)
embed_dim = x1.shape[-1]... | code_fim | hard | {
"lang": "python",
"repo": "initzhang/Hetu",
"path": "/examples/ctr/tf_models/tf_dcn_criteo.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> with tf.device('/gpu:0'):
flatten = tf.reshape(sparse_input_embedding,
(-1, 26*embedding_size))
x = tf.concat((flatten, dense_input), 1)
# CrossNet
cross_output = build_cross_layer(x, num_layers=3, device=device)
... | code_fim | hard | {
"lang": "python",
"repo": "initzhang/Hetu",
"path": "/examples/ctr/tf_models/tf_dcn_criteo.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def _if_else_statement(args=None):
"""
Draw a specific number of spirographs if a command line option is given.
Otherwise, draw 4 spirographs.
Parameters
----------
args : argparse.Namespace, optional
Optional command line arguments.
Returns
-------
None
"... | code_fim | hard | {
"lang": "python",
"repo": "hestrang1993/pp",
"path": "/spirograph/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # checks args and draw
if args.sparams:
parameters = [float(x) for x in args.sparams]
# draw spirograph with given parameters
# black by default
col = (0.0, 0.0, 0.0)
spirograph = Spirograph(0, 0, col, *parameters)
spirograph.draw()
else:
... | code_fim | hard | {
"lang": "python",
"repo": "hestrang1993/pp",
"path": "/spirograph/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hestrang1993/pp path: /spirograph/main.py
"""
The :mod:`spirograph.main` module contains the :function:`main`.
The :function:`main` function, along with it's associated helper functions, will make it easy to draw multiple
spirographs from the command line.
"""
import argparse
import turtle
from... | code_fim | hard | {
"lang": "python",
"repo": "hestrang1993/pp",
"path": "/spirograph/main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Run LBP while loop
while cond_fun(alpha_beta_iteration):
alpha_beta_iteration = body_fun(alpha_beta_iteration)
alphabeta, _ = alpha_beta_iteration
# Compute two consecutive marginals
marginal = marginal_from_alphabeta(alphabeta)
marginal_plus_one_iteration = marginal_from_alphabeta(lbp... | code_fim | hard | {
"lang": "python",
"repo": "Ayoob7/google-research",
"path": "/grouptesting/samplers/loopy_belief_propagation.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ayoob7/google-research path: /grouptesting/samplers/loopy_belief_propagation.py
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a co... | code_fim | hard | {
"lang": "python",
"repo": "Ayoob7/google-research",
"path": "/grouptesting/samplers/loopy_belief_propagation.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
rng : random PRNG key
state : state object containing all relevant information to produce sample
Returns:
a measure of the quality of convergence, here gap_between_consecutives
also updates particle_weights and particles members.
"""
self.particle_weights = ... | code_fim | hard | {
"lang": "python",
"repo": "Ayoob7/google-research",
"path": "/grouptesting/samplers/loopy_belief_propagation.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>ft_xor_sum = num ^ left_xor_sum
else:
right_xor_sum = num ^ right_xor_sum
return [left_xor_sum, right_xor_sum]<|fim_prefix|># repo: pauvrepetit/leetcode path: /others/剑指 Offer/56/main.py
from typing import List
class Solution:
def singleNumbers(self, nums: List[in... | code_fim | hard | {
"lang": "python",
"repo": "pauvrepetit/leetcode",
"path": "/others/剑指 Offer/56/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pauvrepetit/leetcode path: /others/剑指 Offer/56/main.py
from typing import List
class Solution:
def singleNumbers(self, nums: List[int]) -> List[int]:
xor_sum = 0
for num in nums:
xor_sum = num ^ xor_sum
count = 0
while xor_sum & 1 == 0:
... | code_fim | hard | {
"lang": "python",
"repo": "pauvrepetit/leetcode",
"path": "/others/剑指 Offer/56/main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Colk-tech/ColkSmallUtils path: /ChangeGitProfile/main.py
import sys
import shutil
import os
import initializer
args = sys.argv
home = str(os.environ['HOME'])
gitconfigs = home + "/.gitconfigs"
if not os.path.exists(gitconfigs):
print("It seems that you are running this script at first tim... | code_fim | medium | {
"lang": "python",
"repo": "Colk-tech/ColkSmallUtils",
"path": "/ChangeGitProfile/main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>if not len(args) == 2:
print("Error!")
raise IndexError("Please specify only one argument")
if not os.path.exists(gitconfigs + "/" + args[1]):
print("Configure '{}' not found".format(args[1]))
exit()
else:
if os.path.exists(home + "/.gitconfig"):
try:
os.remove(ho... | code_fim | hard | {
"lang": "python",
"repo": "Colk-tech/ColkSmallUtils",
"path": "/ChangeGitProfile/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Selecting the best individuals in the current generation as
# parents for producing the offspring of the next generation.
parents = numpy.empty((num_parents, pop.shape[1]))
for parent_num in range(num_parents):
max_fitness_idx = numpy.where(fitness == numpy.max(fitness))
... | code_fim | hard | {
"lang": "python",
"repo": "AndreiPi/MetodeDeNatura",
"path": "/GA Versions/manual_ga/ga.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def crossover(parents, offspring_size):
offspring = numpy.empty(offspring_size)
# The point at which crossover takes place between two parents. Usually, it is at the center.
crossover_point = numpy.uint32(offspring_size[1] / 2)
for k in range(offspring_size[0]):
# Index of the fir... | code_fim | hard | {
"lang": "python",
"repo": "AndreiPi/MetodeDeNatura",
"path": "/GA Versions/manual_ga/ga.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AndreiPi/MetodeDeNatura path: /GA Versions/manual_ga/ga.py
import numpy
import random
# Converting each solution from matrix to vector.
def mat_to_vector(mat_pop_weights):
pop_weights_vector = []
for sol_idx in range(mat_pop_weights.shape[0]):
curr_vector = []
for layer_... | code_fim | hard | {
"lang": "python",
"repo": "AndreiPi/MetodeDeNatura",
"path": "/GA Versions/manual_ga/ga.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # and/or store regions in HDF5 format
hdf5 = HDF5File(mesh.mpi_comm(),
"results/h5-ernie-parcellation.h5", "w")
hdf5.write(mesh, "/mesh")
hdf5.write(regions, "/regions")
hdf5.close()
map_parcellation_to_mesh("wmparc.mgz", "ernie-brain-32.xdmf")<|fim_prefix|># r... | code_fim | hard | {
"lang": "python",
"repo": "elepiersan/mri2fem",
"path": "/mri2fem/mri2fem/chp4/map_parcellation.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: elepiersan/mri2fem path: /mri2fem/mri2fem/chp4/map_parcellation.py
import numpy
import nibabel
from nibabel.affines import apply_affine
from dolfin import *
def map_parcellation_to_mesh(parcfile, meshfile):
# Load image from the parcellation file,
# extract its data and output its dimens... | code_fim | hard | {
"lang": "python",
"repo": "elepiersan/mri2fem",
"path": "/mri2fem/mri2fem/chp4/map_parcellation.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> parser.add_argument(
'--source-directory',
default='.',
help='The path to directory containing the source code for the build.')
parser.add_argument(
'--output-file',
default='source-context.json',
help='The path to the output file containing the sour... | code_fim | hard | {
"lang": "python",
"repo": "twistedpair/google-cloud-sdk",
"path": "/google-cloud-sdk/lib/googlecloudsdk/appengine/app_commands/gen_repo_info_file.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: twistedpair/google-cloud-sdk path: /google-cloud-sdk/lib/googlecloudsdk/appengine/app_commands/gen_repo_info_file.py
# Copyright 2014 Google Inc. All Rights Reserved.
"""The gen_repo_info_file command."""
from googlecloudsdk.api_lib.source import generate_source_context
from googlecloudsdk.cal... | code_fim | medium | {
"lang": "python",
"repo": "twistedpair/google-cloud-sdk",
"path": "/google-cloud-sdk/lib/googlecloudsdk/appengine/app_commands/gen_repo_info_file.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: deepampatel/jina path: /jina/parsers/peapods/pod.py
import argparse
from jina.enums import PollingType, SchedulerType, PodRoleType
from jina.parsers.helper import add_arg_group, _SHOW_ALL_ARGS
def mixin_base_pod_parser(parser):
"""Mixing in arguments required by :class:`BasePod` into the g... | code_fim | hard | {
"lang": "python",
"repo": "deepampatel/jina",
"path": "/jina/parsers/peapods/pod.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # hidden CLI used for internal only
gp.add_argument('--pod-role', type=PodRoleType.from_string, choices=list(PodRoleType),
help='The role of this pod in the flow' if _SHOW_ALL_ARGS else argparse.SUPPRESS)<|fim_prefix|># repo: deepampatel/jina path: /jina/parsers/peapods/pod.p... | code_fim | hard | {
"lang": "python",
"repo": "deepampatel/jina",
"path": "/jina/parsers/peapods/pod.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>tractor as Extractor
elif module == 'tensorrt':
from .extract_tensorrt import TensorrtExtractor as Extractor
else:
raise ImportError(
'module must be in one of [onnx, caffemodel, netdef, graphdef, h5, mxnetparams, savedmodel, torchscript, pmml, tensorrt]'
)<|fim_prefix|># repo: judgeee... | code_fim | hard | {
"lang": "python",
"repo": "judgeeeeee/klever-model-registry",
"path": "/scripts/extract/extractor/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: judgeeeeee/klever-model-registry path: /scripts/extract/extractor/__init__.py
import os
module = os.environ.get('EXTRACTOR', 'NULL')
if module == 'onnx':
from .extract_onnx import OnnxExtractor as Extractor
elif module == 'caffemodel':
from .extract_caffe import CaffeExtractor as Extract... | code_fim | hard | {
"lang": "python",
"repo": "judgeeeeee/klever-model-registry",
"path": "/scripts/extract/extractor/__init__.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: run-ai/runai path: /examples/elastic/keras/mnist.py
# horovodrun -np `nvidia-smi --list-gpus | wc -l` -H localhost:`nvidia-smi --list-gpus | wc -l` python examples/elastic/keras/mnist.py
from __future__ import print_function
import keras
from keras.models import Sequential
from keras.layers imp... | code_fim | hard | {
"lang": "python",
"repo": "run-ai/runai",
"path": "/examples/elastic/keras/mnist.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>NUM_CLASSES = 10
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = keras.utils.to_categorical(y_tr... | code_fim | medium | {
"lang": "python",
"repo": "run-ai/runai",
"path": "/examples/elastic/keras/mnist.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>model.compile(
loss='categorical_crossentropy',
optimizer=keras.optimizers.Adadelta(lr=1.0), # pass any valid Keras optimizer
metrics=['accuracy']
)
model.fit(x_train, y_train,
batch_size=runai.elastic.batch_size, # use the calculated configuration (batch size in this case... | code_fim | hard | {
"lang": "python",
"repo": "run-ai/runai",
"path": "/examples/elastic/keras/mnist.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vas3k/vas3k.club path: /common/data/countries.py
COUNTRIES = [
("Россия", "Россия"),
("Украина", "Украина"),
("Беларусь", "Беларусь"),
("Казахстан", "Казахстан"),
("Абхазия", "Абхазия"),
("Австралия", "Австралия"),
("Австрия", "Австрия"),
("Азербайджан", "Азербайдж... | code_fim | hard | {
"lang": "python",
"repo": "vas3k/vas3k.club",
"path": "/common/data/countries.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>нт-Люсия"),
("Сент-Пьер и Микелон", "Сент-Пьер и Микелон"),
("Сербия", "Сербия"),
("Сингапур", "Сингапур"),
("Синт-Мартен", "Синт-Мартен"),
("Сирийская Арабская Республика", "Сирийская Арабская Республика"),
("Словакия", "Словакия"),
("Словения", "Словения"),
("Соломоновы о... | code_fim | hard | {
"lang": "python",
"repo": "vas3k/vas3k.club",
"path": "/common/data/countries.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Pruning variables by given ratios.
Args:
ratios(dict<str, float>): The key is the name of variable to be pruned and the
value is the pruned ratio.
axis(int): The dimension to be pruned on.
Returns:
... | code_fim | hard | {
"lang": "python",
"repo": "PaddlePaddle/PaddleSlim",
"path": "/paddleslim/dygraph/prune/pruner.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: PaddlePaddle/PaddleSlim path: /paddleslim/dygraph/prune/pruner.py
import os
import pickle
import numpy as np
import logging
from .pruning_plan import PruningPlan
from paddleslim.common import get_logger
__all__ = ["Pruner"]
_logger = get_logger(__name__, level=logging.INFO)
class Pruner(objec... | code_fim | hard | {
"lang": "python",
"repo": "PaddlePaddle/PaddleSlim",
"path": "/paddleslim/dygraph/prune/pruner.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>badgeSwappers = badge_swappers()
print("\n\n------------ Stats ------------")
print("You're missing $"+str(round(sumFunds,2)*-1)+" Canadian Rupees from your wallet")
print("You've swapped with "+str(len(badgeSwappers.keys()))+" different friends since May 3rd 🏓")
print("You've swapped with "+str(len(swap... | code_fim | hard | {
"lang": "python",
"repo": "dlabrie/shakescripts-python",
"path": "/pyscripts/all_swaps_short.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dlabrie/shakescripts-python path: /pyscripts/all_swaps_short.py
from modules.shakepay import *
updateTransactions()
swaps = all_swaps()
swapsSummary = {}
for swapper in swaps:
if swaps[swapper] != 0:
transactions = swapperTransactions(swapper)
lastTransaction = list(transac... | code_fim | medium | {
"lang": "python",
"repo": "dlabrie/shakescripts-python",
"path": "/pyscripts/all_swaps_short.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for iter_ in tqdm(range(settings.num_inference_samples), ncols=100):
arr_preds, arr_target = get_batch_predictions(
rnn, packed, target_tensor
)
# Revert sorting that occurs in get_batch_predictions
... | code_fim | hard | {
"lang": "python",
"repo": "supernnova/SuperNNova",
"path": "/supernnova/validation/validate_rnn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: supernnova/SuperNNova path: /supernnova/validation/validate_rnn.py
s lu
def find_idx(array, value):
"""Utility to find the index of the element of ``array`` that most closely
matches ``value``
Args:
array (np.array): The array in which to search
value (float): The v... | code_fim | hard | {
"lang": "python",
"repo": "supernnova/SuperNNova",
"path": "/supernnova/validation/validate_rnn.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: supernnova/SuperNNova path: /supernnova/validation/validate_rnn.py
the element of ``array`` that most closely
matches ``value``
Args:
array (np.array): The array in which to search
value (float): The value for which we are looking for a match
Returns:
(int) t... | code_fim | hard | {
"lang": "python",
"repo": "supernnova/SuperNNova",
"path": "/supernnova/validation/validate_rnn.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> #Load regressor
model = pickle.load(open('data/regressor.sav', 'rb'))
#Test against the test database
predictions = model.predict(X_test)
#Calculate mean distance between prediction and real values
i = 0
mean_dist = 0.0
while(i < len(predictions)):
prediction = predictions[i]
real = Y_test[i... | code_fim | hard | {
"lang": "python",
"repo": "urbanoanderson/ufpe-graduation-thesis",
"path": "/src/wcnc_paper/experiments/extra/test_regressor.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: urbanoanderson/ufpe-graduation-thesis path: /src/wcnc_paper/experiments/extra/test_regressor.py
#!/usr/bin/python
# -*- coding: utf-8 -*-
#Utils
import math
import pickle
#Custom Classes
from measurement import *
from erb import *
def GetRegressorArrays(measurement_list):
X = []
Y = []
<|fi... | code_fim | hard | {
"lang": "python",
"repo": "urbanoanderson/ufpe-graduation-thesis",
"path": "/src/wcnc_paper/experiments/extra/test_regressor.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if __name__ == "__main__":
main()
"""
Ubuntu 18.04, CPython 3.6.9, 48 core machine:
count: 1000
cv2: 0.000188s
np: 0.002309s
"""<|fim_prefix|># repo: EricCousineau-TRI/repro path: /bug/opencv_cvtcolor_slow/repro.py
import sys
import timeit
import cv2
import numpy as np
def main():... | code_fim | hard | {
"lang": "python",
"repo": "EricCousineau-TRI/repro",
"path": "/bug/opencv_cvtcolor_slow/repro.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: EricCousineau-TRI/repro path: /bug/opencv_cvtcolor_slow/repro.py
import sys
import timeit
import cv2
import numpy as np
def main():
np.random.seed(0)
rgb = np.random.randint(0, high=255, size=(480, 848, 3), dtype=np.uint8)
bgr = np.zeros_like(rgb)
count = 1000
scope = dict... | code_fim | medium | {
"lang": "python",
"repo": "EricCousineau-TRI/repro",
"path": "/bug/opencv_cvtcolor_slow/repro.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jpsampaio/ExEcommIT path: /Ex82.py
l1 = []
l2 = []
l3 = []
r = 's'
while r == 's':
x = int(input('Digite um número: '))
if x % 2 == 0 and x != 0:
l2.append(x)
elif x % 2 == 1 and x != 0:
l3.append(x)
l1.append(x)
r = str(input('Quer c<|fim_suffix|>odos os valor... | code_fim | medium | {
"lang": "python",
"repo": "jpsampaio/ExEcommIT",
"path": "/Ex82.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>odos os valores é {l1}')
print(f'A lista com os valores pares é {l2}')
print(f'A lista com os valores impares é {l3}')<|fim_prefix|># repo: jpsampaio/ExEcommIT path: /Ex82.py
l1 = []
l2 = []
l3 = []
r = 's'
while r == 's':
x = int(input('Digite um número: '))
if x % 2 == 0 and x != 0:
l2.... | code_fim | medium | {
"lang": "python",
"repo": "jpsampaio/ExEcommIT",
"path": "/Ex82.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Haider8/oscarine-api path: /app/tests/conftest.py
from typing import Generator
import pytest
from fastapi.testclient import TestClient
from app.db.base import Base
from app.db.session import db_session as db_session_
from app.db.session import engine
from app.main import app
<|fim_suffix|> ... | code_fim | medium | {
"lang": "python",
"repo": "Haider8/oscarine-api",
"path": "/app/tests/conftest.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
@pytest.fixture(scope="module")
def client() -> Generator:
with TestClient(app) as c:
yield c<|fim_prefix|># repo: Haider8/oscarine-api path: /app/tests/conftest.py
from typing import Generator
import pytest
from fastapi.testclient import TestClient
<|fim_middle|>from app.db.base import Ba... | code_fim | hard | {
"lang": "python",
"repo": "Haider8/oscarine-api",
"path": "/app/tests/conftest.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: judebues/softmanage path: /work/yml/Wordprocessing/urls.py
from django.urls import path
from django.conf.urls import url,include
from . import views
urlpatterns = [
url(r'^upload/$', views.upload_file),
u<|fim_suffix|>home_page,name="home"),
url(r"^search/$",views.search,name='searc... | code_fim | medium | {
"lang": "python",
"repo": "judebues/softmanage",
"path": "/work/yml/Wordprocessing/urls.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>home_page,name="home"),
url(r"^search/$",views.search,name='search'),
]<|fim_prefix|># repo: judebues/softmanage path: /work/yml/Wordprocessing/urls.py
from django.urls import path
from django.conf.urls import url,include
from . import views
urlpatterns = [
url(r'^upload/$', views.upload_fil... | code_fim | medium | {
"lang": "python",
"repo": "judebues/softmanage",
"path": "/work/yml/Wordprocessing/urls.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> utt_elem = etree.Element(self.TEI + "u",
who=self.speaker, nsmap=self.NSMAP)
utt_elem.text = self.speech
# return etree.tostring(utt_elem)
return utt_elem
def append(self, text):
# self.speech = self.speech + "\n\n" + text
... | code_fim | hard | {
"lang": "python",
"repo": "agile-humanities/ddhi-encoder",
"path": "/src/ddhi_encoder/utterance.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: agile-humanities/ddhi-encoder path: /src/ddhi_encoder/utterance.py
# -*- coding: utf-8 -*-
# utterance.py
from lxml import etree
import re
class Utterance:
TEI_NAMESPACE = "http://www.tei-c.org/ns/1.0"
TEI = "{%s}" % TEI_NAMESPACE
NSMAP = {None: TEI_NAMESPACE} # default namespace
... | code_fim | hard | {
"lang": "python",
"repo": "agile-humanities/ddhi-encoder",
"path": "/src/ddhi_encoder/utterance.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def dfs(root: Optional[TreeNode]) -> T:
if not root:
return T(-1, -1, -1)
left = dfs(root.left)
right = dfs(root.right)
leftZigZag = left.rightMax + 1
rightZigZag = right.leftMax + 1
subtreeMax = max(leftZigZag, rightZigZag,
left.subtr... | code_fim | medium | {
"lang": "python",
"repo": "walkccc/LeetCode",
"path": "/solutions/1372. Longest ZigZag Path in a Binary Tree/1372.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def longestZigZag(self, root: Optional[TreeNode]) -> int:
def dfs(root: Optional[TreeNode]) -> T:
if not root:
return T(-1, -1, -1)
left = dfs(root.left)
right = dfs(root.right)
leftZigZag = left.rightMax + 1
rightZigZag = right.leftMax + 1
subtreeMax = ma... | code_fim | medium | {
"lang": "python",
"repo": "walkccc/LeetCode",
"path": "/solutions/1372. Longest ZigZag Path in a Binary Tree/1372.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: walkccc/LeetCode path: /solutions/1372. Longest ZigZag Path in a Binary Tree/1372.py
class T:
def __init__(self, leftMax: int, rightMax: int, subtreeMax: int):
self.leftMax = leftMax
self.rightMax = rightMax
self.subtreeMax = subtreeMax
<|fim_suffix|> if not root:
retu... | code_fim | medium | {
"lang": "python",
"repo": "walkccc/LeetCode",
"path": "/solutions/1372. Longest ZigZag Path in a Binary Tree/1372.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if loaded_data[xf].shape[1] == len(loaded_data[yf]):
loaded_data[xf] = np.transpose(loaded_data[xf])
if sw in data_fields:
loaded_data[sw] = np.array(r_data[sw]).flatten()
if 'variable_names' in data_fields:
loaded_data['variable_names'... | code_fim | hard | {
"lang": "python",
"repo": "ustunb/dcptree",
"path": "/dcptree/data_io.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ustunb/dcptree path: /dcptree/data_io.py
".weights" (if dataset_file ends in ".data")
"_weights.csv" (if dataset_file ends in "_data.csv")
include_intercept if True then an intercept is added to the X matrix
Returns
-------... | code_fim | hard | {
"lang": "python",
"repo": "ustunb/dcptree",
"path": "/dcptree/data_io.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> if 'format' in data_fields:
loaded_data['format'] = np.array(r_data['format'])[0]
if 'partitions' in data_fields:
loaded_data['partitions'] = np.array(rn.r.data['partitions']).tolist()
cvindices = _load_cvindices_from_rdata(file_name)
data = set_defaults_for_data(loaded_d... | code_fim | hard | {
"lang": "python",
"repo": "ustunb/dcptree",
"path": "/dcptree/data_io.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == '__main__':
dump(sys.stdin, sys.stdout)<|fim_prefix|># repo: klaasjacobdevries/kyaml path: /python/test/dump.py
#!/usr/bin/python
import pykyaml as kyaml
import sys
def dump(input, output):
parser = kyaml.parser(input)
root = parser.parse()
<|fim_middle|> output.write('%s... | code_fim | easy | {
"lang": "python",
"repo": "klaasjacobdevries/kyaml",
"path": "/python/test/dump.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: klaasjacobdevries/kyaml path: /python/test/dump.py
#!/usr/bin/python
import pykyaml as kyaml
import sys
def dump(input, output):
parser = kyaml.parser(input)
root = parser.parse()
<|fim_suffix|>if __name__ == '__main__':
dump(sys.stdin, sys.stdout)<|fim_middle|> output.write('%s... | code_fim | easy | {
"lang": "python",
"repo": "klaasjacobdevries/kyaml",
"path": "/python/test/dump.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> output.write('%s\n' % root)
if __name__ == '__main__':
dump(sys.stdin, sys.stdout)<|fim_prefix|># repo: klaasjacobdevries/kyaml path: /python/test/dump.py
#!/usr/bin/python
import pykyaml as kyaml
import sys
def dump(input, output):
<|fim_middle|> parser = kyaml.parser(input)
root = par... | code_fim | medium | {
"lang": "python",
"repo": "klaasjacobdevries/kyaml",
"path": "/python/test/dump.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>t(f'A primeira letra A aparece na posição {juntar.find("A") + 1} e a última letra A aparece na posição {juntar.rfind("A") + 1}.')<|fim_prefix|># repo: LarissaMidori/curso_em_video path: /exercicio026.py
''' Faça um programa que leia uma frase pelo teclado e mostre quantas vezes aparece a letra “A”, em qu... | code_fim | medium | {
"lang": "python",
"repo": "LarissaMidori/curso_em_video",
"path": "/exercicio026.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LarissaMidori/curso_em_video path: /exercicio026.py
''' Faça um programa que leia uma frase pelo teclado e mostre quantas vezes aparece a letra “A”, em que posição ela aparece a primeira vez e em que posição ela aparece a última vez. '''
frase = str(input('Digite uma frase: ')).upper().strip()
s... | code_fim | medium | {
"lang": "python",
"repo": "LarissaMidori/curso_em_video",
"path": "/exercicio026.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> posicao = self.pesquisar(valor)
if posicao == -1:
return -1
else:
for i in range(posicao, self.ultima_posicao):
self.valores[i] = self.valores[i+1]
self.ultima_posicao -= 1
vetor = VetorNaoOrdenado(5)
vetor.insere(2)
vetor.insere(3)
vetor.insere(8)
vetor.ins... | code_fim | hard | {
"lang": "python",
"repo": "AlissonRaphael/algorithm_and_data_structures",
"path": "/02_vetor_ordenado_pesquisa_linear.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AlissonRaphael/algorithm_and_data_structures path: /02_vetor_ordenado_pesquisa_linear.py
import numpy as np
class VetorNaoOrdenado:
def __init__(self, capacidade):
self.capacidade = capacidade
self.ultima_posicao = -1
self.valores = np.empty(self.capacidade, dtype=int)
# BigO =>... | code_fim | medium | {
"lang": "python",
"repo": "AlissonRaphael/algorithm_and_data_structures",
"path": "/02_vetor_ordenado_pesquisa_linear.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if i == self.ultima_posicao:
return -1
# BigO => O(n)
def excluir(self, valor):
posicao = self.pesquisar(valor)
if posicao == -1:
return -1
else:
for i in range(posicao, self.ultima_posicao):
self.valores[i] = self.valores[i+1]
self.ultima_po... | code_fim | hard | {
"lang": "python",
"repo": "AlissonRaphael/algorithm_and_data_structures",
"path": "/02_vetor_ordenado_pesquisa_linear.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>_assignments",
"check_mixture_model",
"check_component_model"
]<|fim_prefix|># repo: thetianshuhuang/bmcc path: /bmcc/util/__init__.py
from .get_params import get_params
from .type_check import (
check<|fim_middle|>_data,
check_assignments,
check_mixture_model,
check_component_mod... | code_fim | medium | {
"lang": "python",
"repo": "thetianshuhuang/bmcc",
"path": "/bmcc/util/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
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