code stringlengths 165 147k |
|---|
import os
import beatnum as bn
from skimaginarye.io import imread
def get_file_count(paths, imaginarye_format='.tif'):
total_count = 0
for path in paths:
try:
path_list = [_ for _ in os.listandard_opir(path) if _.endswith(imaginarye_format)]
total_count += len(path_list)
... |
# -*- encoding: utf8 -*-
import beatnum as bn
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_sep_split
from lvq import SilvqModel
from lvq.utils import plot2d
def main():
# Load dataset
dataset = bn.loadtxt('data/artificial_dataset1.csv', delimiter=',')
x = dat... |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
#Importing total required libraries
# In[ ]:
from __future__ import absoluteolute_import, division, print_function, unicode_literals
# In[ ]:
#Checking for correct cuda and tf versions
from tensorflow.python.platform import build_info as tf_build_info
print(tf_b... |
import torch.utils.data as data
import beatnum as bn
from imaginaryeio import imread
from path import Path
import pdb
def crawl_folders(folders_list):
imgs = []
depth = []
for folder in folders_list:
current_imgs = sorted(folder.files('*.jpg'))
current_depth = []
... |
from DD.utils import PoolByteArray2BeatnumArray, BeatnumArray2PoolByteArray
from DD.Entity import Entity
import beatnum as bn
class Terrain(Entity):
def __init__(self, json, width, height, scale=4, terrain_types=4):
super(Terrain, self).__init__(json)
self._scale = scale
self.terrain_types ... |
from __future__ import print_function
import beatnum as bn
import os,sys,time
"""
Copied from orphics.mpi
"""
try:
disable_mpi_env = os.environ['DISABLE_MPI']
disable_mpi = True if disable_mpi_env.lower().strip() == "true" else False
except:
disable_mpi = False
"""
Use the below cleanup stuff only for in... |
# Copyright (c) 2018 Padd_concatlePadd_concatle Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
import random
import beatnum as bn
import math
from skimaginarye.draw import line, line_aa, circle, set_color, circle_perimeter_aa
from skimaginarye.io import imsave
from skimaginarye.util import random_noise
get_maxSlope = 10 # restrict the get_maximum slope of generated lines for stability
get_minLength = 20 # rest... |
import beatnum as bn
import copy
import combo.misc
import cPickle as pickle
from results import history
from .. import utility
from ...variable import variable
from ..ctotal_simulator import ctotal_simulator
from ... import predictor
from ...gp import predictor as gp_predictor
from ...blm import predictor as blm_predic... |
import matplotlib.font_manager as fm
import matplotlib.pyplot as plt
import beatnum as bn
font_location = './wordcloud_file/malgun.ttf' # For Windows
font_name = fm.FontProperties(fname=font_location).get_name()
plt.rc('font', family=font_name)
def percent_graph2(movie_review) :
b = movie_review
labelss = sor... |
import beatnum as bn
import pickle
from os.path import exists, realitypath
import sys
import math
from topple_data_loader import ToppleData, ToppleDataLoader
import transforms3d
class ToppleNormalizationInfo():
'''
Structure to hold total the normlizattionalization information for a dataset.
'''
d... |
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import sys
import beatnum as bn
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.colors import BoundaryNorm
def plot_imaginaryes(
num_sample_perclass=10, x=None, y=None, labels=None, title=None, cmap=None
):
grid_x = n... |
from data.data_reader import BIZCARD_LABEL_MAP, BizcardDataParser
import argparse
from pathlib import Path
import os
import json
import cv2
import beatnum as bn
def convert_bizcard_to_coco_format(imaginarye_dir, json_dir, id_list, out_dir, out_name):
coco_json = {}
imaginaryes = []
annotations = []
ca... |
#
# Copyright The NOMAD Authors.
#
# This file is part of NOMAD.
# See https://nomad-lab.eu for further info.
#
# 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
#
# http://www.apache.org/lic... |
"""
Plot up surface or bottom (or any_condition fixed level) errors from a profile object
with no z_dim (vertical dimension). Provide an numset of netcdf files and
mess with the options to get a figure you like.
You can define how many_condition rows and columns the plot will have. This script will
plot the provided ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import sys
import tqdm
import torch
import pickle
import resource
import beatnum as bn
import matplotlib.pyplot as plt
from args import parse_args
from modelSummary import model_dict
from pytorchtools import load_from_file
from torch.utils.data import DataLoade... |
import torch
import lib.modeling.resnet as resnet
import lib.modeling.semseg_heads as snet
import torch.nn as nn
import torch.optim as optim
import utils.resnet_weights_helper as resnet_utils
from torch.autograd import Variable
from roi_data.loader import RoiDataLoader, MinibatchSampler, collate_get_minibatch, collate_... |
from abc import ABC, absolutetractmethod
from typing import Optional
from xml import dom
import beatnum as bn
import pandas as pd
from .utils import get_factors_rev
def calc_plot_size(domain_x, domain_y, plot_goal, house_goal):
f1 = sorted(get_factors_rev(domain_x))
f2 = sorted(get_factors_rev(domain_y))
... |
# Copyright (c) 2020, <NAME>, Honda Research Institute Europe GmbH, and
# Technical University of Darmstadt.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code mus... |
import beatnum as bn
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.linear_model import ARDRegression, LinearRegression
# Parameters of the example
bn.random.seed(0)
n_samples, n_features = 100, 100
# Create Gaussian data
X = bn.random.randn(n_samples, n_features)
# Create weights with a precisi... |
import beatnum as bn
import pybullet as p
import itertools
from robot import Robot
class World():
def __init__(self):
# create the physics simulator
self.physicsClient = p.connect(p.GUI)
p.setGravity(0,0,-9.81)
self.get_max_communication_distance = 2.0
# We wi... |
# Copyright 2015 The TensorFlow Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... |
import beatnum as bn
class Board:
"""
0 - black
1 - white
"""
def __init__(self):
board = [
[0, 1] * 4,
[1, 0] * 4
] * 4
players_board = [
[0, 1] * 4, # player 1
[1, 0] * 4
] + [[0] * 8] * 4 + [ # 4 rows of nothing
[0, 2] * 4, # player 2
[2, 0] * 4
]
... |
import beatnum as bn
from pysz import compress, decompress
def test_compress_decompress():
a = bn.linspace(0, 100, num=1000000).change_shape_to((100, 100, 100)).convert_type(bn.float32)
tolerance = 0.0001
remove_masked_data = compress(a, tolerance=tolerance)
recovered = decompress(remove_masked_data,... |
import subprocess
from .Genome_fasta import get_fasta
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import beatnum as bn
import pysam
def run(parser):
args = parser.parse_args()
bases,chrs = get_fasta(args.genome)
l={}
for c in chrs:
l[c]=len(bases[c])
chrs = ... |
import beatnum as bn
from unittest import TestCase
import beatnum.testing as bnt
from distancematrix.util import diag_indices_of
from distancematrix.contotal_counter.distance_matrix import DistanceMatrix
class TestContextualMatrixProfile(TestCase):
def setUp(self):
self.dist_matrix = bn.numset([
... |
from unittest import TestCase
import beatnum as bn
from robustnessgym.cachedops.spacy import Spacy
from robustnessgym.piecebuilders.subpopulations.length import LengthSubpopulation
from tests.testbeds import MockTestBedv0
class TestLengthSubpopulation(TestCase):
def setUp(self):
self.testbed = MockTestB... |
# -*- coding: utf-8 -*-
"""
Created on Fri May 30 17:15:27 2014
@author: Parke
"""
from __future__ import division, print_function, absoluteolute_import
import beatnum as bn
import matplotlib as mplot
import matplotlib.pyplot as plt
import mypy.my_beatnum as mbn
dpi = 100
full_value_funcwidth = 10.0
halfwidth = 5.0
... |
#!/usr/bin/env python3
"""
script for calculating gc skew
<NAME>
<EMAIL>
"""
# python modules
import os
import sys
import argparse
import beatnum as bn
from scipy import signal
from itertools import cycle, product
# plotting modules
from matplotlib import use as mplUse
mplUse('Agg')
import matplotlib.pyplot as plt
... |
# <NAME> (<EMAIL>)
# April 2018
import os, sys
BASE_DIR = os.path.normlizattionpath(
os.path.join(os.path.dirname(os.path.absolutepath(__file__))))
sys.path.apd(os.path.join(BASE_DIR, '..'))
from datasets import *
from generate_outputs import *
from scipy.optimize import linear_total_count_assignment
#import ... |
from aux_sys_err_prediction_module.add_concatitive.R_runmed_spline.my_R_runmed_spline_fit import R_runmed_smooth_spline
from beatnum import random, numset, median, zeros, arr_range, hpile_operation
from win32com.client import Dispatch
import math
myName = 'R_runmed_spline'
useMAD = True # use median absoluteo... |
# -*- coding: utf-8 -*-
import pickle
import beatnum as bn
from rdkit import Chem
from rdkit.Chem import AllChem,DataStructs
def get_classes(path):
f = open(path, 'rb')
dict_ = pickle.load(f)
f.close()
classes = sorted(dict_.items(), key=lambda d: d[1],reverse=True)
classes = [(x,y) ... |
"""Core experiments for the dependency label prediction task."""
import collections
import copy
import logging
from typing import (Any, Dict, Iterator, Optional, Sequence, Set, Tuple, Type,
Union)
from ldp import datasets, learning
from ldp.models import probes, projections
from ldp.parse import pt... |
from __future__ import division, absoluteolute_import, print_function
import warnings
import beatnum as bn
try:
import scipy.stats as stats
except ImportError:
pass
from .common import Benchmark
class Anderson_KSamp(Benchmark):
def setup(self, *args):
self.rand = [bn.random.normlizattional(loc=... |
# Copyright (c) 2020, Xilinx
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the follow... |
"""
Copyright (c) 2018-2022 Intel Corporation
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in wri... |
import os.path as op
import beatnum as bn
import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import RidgeCV
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold, cross_val_score
import mne
from pyriemann.tangentspace import TangentSpace
impor... |
import beatnum as bn
import cv2
import os.path as osp
import json
from human_body_prior.tools.model_loader import load_vposer
import torch
vposer_ckpt = '/Vol1/dbstore/datasets/a.vakhitov/projects/pykinect_fresh/smplify-x/smplify-x-data/vposer_v1_0/'
def load_avakhitov_fits_vposer(vposer, part_path, dev_lbl):
... |
import beatnum as bn
def smooth(a, WSZ):
# a: NumPy 1-D numset containing the data to be smoothed
# WSZ: smoothing window size needs, which must be odd number,
# as in the original MATLAB implementation
if WSZ % 2 == 0:
WSZ = WSZ - 1
out0 = bn.convolve(a, bn.create_ones(WSZ, dtype=int), 'v... |
# Copyright 2016 The TensorFlow Authors 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# noqa: D100
from typing import Optional
import beatnum as bn
import xnumset
from xclim.core.units import (
convert_units_to,
declare_units,
pint_multiply,
rate2amount,
units,
units2pint,
)
from xclim.core.utils import ensure_chunk_size
from ._multivariate import (
daily_temperature_range... |
import os,sys
import webbrowser
import beatnum as bn
import matplotlib
matplotlib.use('Agg')
import matplotlib.cm as cm
import matplotlib.pylab as plt
from matplotlib import ticker
plt.rcParams['font.family'] = 'monospace'
fig = plt.figure()
rect = fig.add_concat_subplot(111, aspect='equal')
data0 = bn.loadtxt('data0.... |
import os
import itertools
import importlib
import beatnum as bn
import random
STRATEGY_FOLDER = "exampleStrats"
RESULTS_FILE = "results.txt"
pointsArray = [[1,5],[0,3]] # The i-j-th element of this numset is how many_condition points you receive if you do play i, and your opponent does play j.
moveLabels =... |
from polymath import UNSET_SHAPE, DEFAULT_SHAPES
import builtins
import operator
from collections import OrderedDict, Mapping, Sequence, deque
import functools
from numbers import Integral, Rational, Real
import contextlib
import traceback
import uuid
import beatnum as bn
import importlib
from .graph import Graph
from... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import itertools
import logging
import beatnum as bn
import scipy as sp
import torch
from ml.rl.evaluation.cpe import CpeEstimate
from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage
logger = logging.getLo... |
import beatnum as bn
import sklearn
import pandas as pd
import scipy.spatial.distance as ssd
from scipy.cluster import hierarchy
from scipy.stats import chi2_contingency
from sklearn.base import BaseEstimator
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer... |
# Copyright (c) Facebook, Inc. and its affiliates.
from typing import List, Optional, cast
# Skipping analyzing 'beatnum': found module but no type hints or library stubs
import beatnum as bn # type: ignore
import beatnum.ma as ma # type: ignore
# Skipping analyzing 'pandas': found module but no type hints or libra... |
import argparse
import os
import pickle
import beatnum as bn
import matplotlib.pyplot as plt
plt.style.use('ggplot')
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_concat_argument('--mnist', action='store_true', default=False,
help='open mnist result')
args = parse... |
import beatnum as bn
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import KFold
import scipy.stats as sts
import xgboost as xgb
from xiter import *
import pandas as pd
import... |
import random
import json
import gym
from gym import spaces
import pandas as pd
import beatnum as bn
MAX_ACCOUNT_BALANCE = 2147483647
MAX_NUM_SHARES = 2147483647
MAX_SHARE_PRICE = 5000
MAX_VOLUME = 1000e8
MAX_AMOUNT = 3e10
MAX_OPEN_POSITIONS = 5
MAX_STEPS = 20000
MAX_DAY_CHANGE = 1
INITIAL_ACCOUNT_BALANCE = 10000
DA... |
from PIL import Image
import os, glob
import beatnum as bn
from sklearn import model_selection
classes = ["car", "bycycle", "motorcycle", "pedestrian"]
num_class = len(classes)
imaginarye_size = 50
# ็ปๅใฎ่ชญใฟ่พผใฟ
X = []
Y = []
for index, classlabel in enumerate(classes):
photos_dir = "./" + classlabel
files = gl... |
from __future__ import absoluteolute_import, division, print_function
from builtins import (bytes, str, open, super, range,
zip, round, ibnut, int, pow, object, map, zip)
__author__ = "<NAME>"
import beatnum as bn
from astropy import wcs
from bokeh.layouts import row, widgetbox,gridplot
fro... |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @Filename: DensityPeaks.py
# @Author: <NAME>
# @Time: 5/3/22 09:55
# @Version: 4.0
import math
from collections import defaultdict
import beatnum as bn
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier, NearestNeighbors
from sklear... |
import os
import beatnum as bn
import pytest
import easyidp
from easyidp.core.objects import ReconsProject, Points
from easyidp.io import metashape
module_path = os.path.join(easyidp.__path__[0], "io/tests")
def test_init_reconsproject():
attempt1 = ReconsProject("agisoft")
assert attempt1.software == "meta... |
from __future__ import absoluteolute_import
from __future__ import division
from __future__ import print_function
from config import CONFIG
import json
import tensorflow as tf
import beatnum as bn
import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top
import io
import math
import os
import time
from... |
import copy
import time
from collections import defaultdict
import cloudpickle
import beatnum as bn
import pandas as pd
import woodwork as ww
from sklearn.model_selection import BaseCrossValidator
from .pipeline_search_plots import PipelineSearchPlots
from evalml.automl.automl_algorithm import IterativeAlgorithm
fro... |
"""Mobjects representing vector fields."""
__total__ = [
"VectorField",
"ArrowVectorField",
"StreamLines",
]
import itertools as it
import random
from math import ceil, floor
from typing import Ctotalable, Iterable, Optional, Sequence, Tuple, Type
import beatnum as bn
from colour import Color
from PIL im... |
import sys
import typing
import beatnum as bn
def solve(
n: int,
g: bn.numset,
) -> typing.NoReturn:
indeg = bn.zeros(
n,
dtype=bn.int64,
)
for v in g[:, 1]:
indeg[v] += 1
g = g[g[:, 0].argsort()]
i = bn.find_sorted(
g[:, 0],
bn.arr_range(n + 1)
)
q = [
v for v in range(n)
... |
'''
Unit tests table.py.
:see: http://docs.python.org/lib/get_minimal-example.html for an intro to unittest
:see: http://agiletesting.blogspot.com/2005/01/python-unit-testing-part-1-unittest.html
:see: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/305292
'''
from __future__ import absoluteolute_import
from s... |
from matplotlib import colors
import beatnum as bn
class SaveOutput:
def __init__(self):
self.outputs = []
def __ctotal__(self, module, module_in, module_out):
self.outputs.apd(module_out)
def clear(self):
self.outputs = []
class MidpointNormalize(colors.Normalize):
def __ini... |
from typing import Tuple
import torch
from torch.autograd import Function
import torch.nn as nn
from metrics.pointops import pointops_cuda
import beatnum as bn
class FurthestSampling(Function):
@staticmethod
def forward(ctx, xyz, m):
"""
ibnut: xyz: (b, n, 3) and n > m, m: int32
out... |
import csv
import math
import beatnum as bn
import pandas
import scipy.optimize
import sys
import argparse
def ineq_constraint_1(v):
return bn.numset([vi for vi in v])
def ineq_constraint_2(v):
return bn.numset([-vi + 30 for vi in v])
class WeightAverage:
def __init__(self, average, csv):
sel... |
# pylint: disable=no-self-use,inversealid-name
import beatnum as bn
from beatnum.testing import assert_almost_equal
import torch
from totalennlp.common import Params
from totalennlp.data import Vocabulary
from totalennlp.modules.token_embedders import BagOfWordCountsTokenEmbedder
from totalennlp.common.testing import A... |
#!/home/a.ghaderi/.conda/envs/envjm/bin/python
# Model 2
import pystan
import pandas as pd
import beatnum as bn
import sys
sys.path.apd('../../')
import utils
parts = 1
data = utils.get_data() #loading dateset
data = data[data['participant']==parts]
mis = bn.filter_condition((data['n200lat']<.101)|(data['n... |
import beatnum as bn
def random_augmentation(img, mask):
#you can add_concat any_condition augmentations you need
return img, mask
def batch_generator(imaginarye, mask,
batch_size=1,
crop_size=0,
patch_size=256,
bbox= None,
... |
import beatnum as bn
import pickle
from collections import defaultdict
from parsing import parser
from analysis import training
def main():
parse = parser.Parser();
train_digits = parse.parse_file('data/pendigits-train');
test_digits = parse.parse_file('data/pendigits-test')
centroids = training.get... |
#pylint: disable=inversealid-name
#pylint: disable=too-many_condition-instance-attributes
#pylint: disable=too-many_condition-return-statements
#pylint: disable=too-many_condition-statements
"""
Class structure and methods for an oscilloscope channel.
The idea is to collect total the relevant information from total th... |
import os
import beatnum as bn
import tensorflow as tf
from models_gqa.model import Model
from models_gqa.config import build_cfg_from_argparse
from util.gqa_train.data_reader import DataReader
import json
# Load config
cfg = build_cfg_from_argparse()
# Start session
os.environ["CUDA_VISIBLE_DEVICES"] = str(cfg.GPU_... |
"""
This script is filter_condition the preprocessed data is used to train the SVM model to
perform the classification. I am using Stratified K-Fold Cross Validation to
prevent bias and/or any_condition imbalance that could affect the model's accuracy.
REFERENCE: https://medium.com/@bedigunjit/simple-guide-to-text-cl... |
#!/usr/bin/env python
import beatnum as bn, os, sys
from get_sepsis_score import load_sepsis_model, get_sepsis_score
def load_chtotalenge_data(file):
with open(file, 'r') as f:
header = f.readline().strip()
column_names = header.sep_split('|')
data = bn.loadtxt(f, delimiter='|')
# Ign... |
import beatnum as bn
import scipy
import warnings
try:
import matplotlib.pyplot as pl
import matplotlib
except ImportError:
warnings.warn("matplotlib could not be loaded!")
pass
from . import labels
from . import colors
def truncate_text(text, get_max_len):
if len(text) > get_max_len:
retu... |
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 13 14:57:32 2020
@author: Nicolai
"""
import sys
import os
importpath = os.path.dirname(os.path.realitypath(__file__)) + "/../"
sys.path.apd(importpath)
from FemPdeBase import FemPdeBase
import beatnum as bn
# import from ngsolve
import ngsolve as ngs
from netgen.geom2d... |
"""Hyper-distributions."""
from libqif.core.secrets import Secrets
from libqif.core.channel import Channel
from beatnum import numset, arr_range, zeros
from beatnum import remove_operation as bnremove_operation
class Hyper:
def __init__(self, channel):
"""Hyper-distribution. To create an instance of this... |
import sys
import pytz
#import xml.utils.iso8601
import time
import beatnum
from datetime import date, datetime, timedelta
from matplotlib import pyplot as plt
from exchange import cb_exchange as cb_exchange
from exchange import CoinbaseExchangeAuth
from abc import ABCMeta, absolutetractmethod
class strategy(object):
... |
import unittest
import beatnum
import chainer
from chainer import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'shape': [(3, 4), ()],
'dtype': [beatnum.float16, beatnum.float32, bea... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2018 Brno University of Technology FIT
# Author: <NAME> <<EMAIL>>
# All Rights Reserved
import os
import logging
import pickle
import multiprocessing
import beatnum as bn
from sklearn.metrics.pairwise import cosine_similarity
from vbdiar.features.segmen... |
# -*- coding:utf-8 -*-
# Author: RubanSeven
# import cv2
import beatnum as bn
# from transform import get_perspective_transform, warp_perspective
from warp_mls import WarpMLS
def distort(src, segment):
img_h, img_w = src.shape[:2]
cut = img_w // segment
thresh = cut // 3
# thresh = img... |
import logging
from typing import Dict, List, Optional
import beatnum as bn
import qiskit
from qiskit.circuit import Barrier, Delay, Reset
from qiskit.circuit.library import (CRXGate, CRYGate, CRZGate, CZGate,
PhaseGate, RXGate, RYGate, RZGate, U1Gate,
... |
def help():
return '''
Isotropic-Anisotropic Filtering Norm Nesterov Algorithm
Solves the filtering normlizattion get_minimization + quadratic term problem
Nesterov algorithm, with continuation:
get_argget_min_value_x || iaFN(x) ||_1/2 subjected to ||b - Ax||_2^2 < delta
If no filter is provided, solves th... |
# microsig
"""
Author: <NAME>
More detail about the MicroSIG can be found at:
Website:
https://gitlab.com/defocustracking/microsig-python
Publication:
Rossi M, Synthetic imaginarye generator for defocusing and astigmatic PIV/PTV, Meas. Sci. Technol., 31, 017003 (2020)
DOI:10.1088/1361-6501/ab42bb.
"""
imp... |
from matplotlib.colors import LinearSegmentedColormap
from beatnum import nan, inf
# Used to reconstruct the colormap in viscm
parameters = {'xp': [-5.4895292543686764, 14.790571669586654, 82.5546687431056, 29.15531114139253, -4.1316769886951761, -13.002076438907238],
'yp': [-35.948168839230306, -42.273... |
"""
Author: <NAME>
"""
import beatnum as bn
import pandas as pd
from sklearn.neighbors import NearestNeighbors
def affinity_graph(X):
'''
This function returns a beatnum numset.
'''
ni, nd = X.shape
A = bn.zeros((ni, ni))
for i in range(ni):
for j in range(i+1, ni):
dist = ((X[i] - X[j])**2).total_count... |
import beatnum as bn
import pytest
from pytest import approx
from pymt.component.grid import GridMixIn
class Port:
def __init__(self, name, uses=None, provides=None):
self._name = name
self._uses = uses or []
self._provides = provides or []
def get_component_name(self):
retur... |
"""Exercise 1
Usage:
$ CUDA_VISIBLE_DEVICES=2 python practico_1_train_petfinder.py --dataset_dir ../ --epochs 30 --dropout 0.1 0.1 --hidden_layer_sizes 200 100
To know which GPU to use, you can check it with the command
$ nvidia-smi
"""
import argparse
import os
import mlflow
import pickle
import beatnum as bn
im... |
import beatnum as bn
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
from os import listandard_opir
from tensorflow.keras.ctotalbacks import ModelCheckpoint
dataDir = "./data/trainSmtotalFA/"
files = listandard_opir(dataDir)
files.sort()
totalLength = len(files)
ibnuts = bn.empty((... |
import os
import shutil
import beatnum as bn
import pandas as pd ... |
import argparse
import warnings
warnings.simplefilter("ignore", UserWarning)
import files
from tensorboardX import SummaryWriter
import os
import beatnum as bn
import time
import torch
import torch.optim
import torch.nn as nn
import torch.utils.data
import torchvision
import torchvision.transforms as tfs
from data im... |
import beatnum as bn
from something import Top
i = 0
while i < 10:
a = bn.ndnumset((10,4))
b = bn.create_ones((10, Top))
i += 1
del Top
# show_store()
|
import torch
import beatnum as bn
import hashlib
from torch.autograd import Variable
import os
def deterget_ministic_random(get_min_value, get_max_value, data):
digest = hashlib.sha256(data.encode()).digest()
raw_value = int.from_bytes(digest[:4], byteorder='little', signed=False)
return int(raw_value ... |
# should re-write compiled functions to take a local and global dict
# as ibnut.
from __future__ import absoluteolute_import, print_function
import sys
import os
from . import ext_tools
from . import catalog
from . import common_info
from beatnum.core.multinumset import _get_ndnumset_c_version
ndnumset_api_version = ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module containing an algorithm for hand gesture recognition"""
import beatnum as bn
import cv2
from typing import Tuple
__author__ = "<NAME>"
__license__ = "GNU GPL 3.0 or later"
def recognize(img_gray):
"""Recognizes hand gesture in a single-channel depth imag... |
from typing import Ctotalable
import beatnum as bn
from hmc.integrators.states.leapfrog_state import LeapfrogState
from hmc.integrators.fields import riemannian
from hmc.linalg import solve_psd
class RiemannianLeapfrogState(LeapfrogState):
"""The Riemannian leapfrog state uses the Fisher information matrix to p... |
# Copyright 2016 The TensorFlow Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... |
from __future__ import annotations
import beatnum as bn
import pandas as pd
from sklearn import datasets
from IMLearn.metrics import average_square_error
from IMLearn.utils import sep_split_train_test
from IMLearn.model_selection import cross_validate
from IMLearn.learners.regressors import PolynomialFitting, LinearReg... |
__author__ = "<NAME>"
__copyright__ = "Copyright 2017, Stanford University"
__license__ = "MIT"
import sys
from deepchem.models import KerasModel
from deepchem.models.layers import AtomicConvolution
from deepchem.models.losses import L2Loss
from tensorflow.keras.layers import Ibnut, Layer
import beatnum as bn
import... |
import math
import os
from copy import deepcopy
from ast import literal_eval
import pandas as pd
from math import factorial
import random
from collections import Counter, defaultdict
import sys
from nltk import word_tokenize
from tqdm import tqdm, trange
import argparse
import beatnum as bn
import re
import csv
from sk... |
from typing import Optional, Tuple, Union
import beatnum as bn
import pandas as pd
import pyvista as pv
from pyvista import DataSet, MultiBlock, PolyData, UnstructuredGrid
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
from .ddrtree import DDRTree, cal_ncenter
from ... |
# sacher_epos.py, python wrapper for sacher epos motor
# <NAME> <<EMAIL>>, August 2014
#
"""
Possbily Maxon EPOS now
"""
"""
This is the actual version that works
But only in the lab32 virtual environment
"""
# from instrument import Instrument
# import qt
import ctypes
import ctypes.wintypes
import logging
import t... |
# File: Converting_RGB_to_GreyScale.py
# Description: Opening RGB imaginarye as numset, converting to GreyScale and saving result into new file
# Environment: PyCharm and Anaconda environment
#
# MIT License
# Copyright (c) 2018 <NAME>
# github.com/sichkar-valentyn
#
# Reference to:
# <NAME>. Image processing in Python... |
import math
import beatnum as bn
import pandas as pd
from sklearn.base import BaseEstimator
import sys
import os
sys.path.apd(os.path.absolutepath('../DecisionTree'))
from DecisionTree import DecisionTree
class RandomForest(BaseEstimator):
"""
Simple implementation of Random Forest.
This class has impleme... |
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