arxiv_id stringlengths 0 16 | text stringlengths 10 1.65M |
|---|---|
#!/usr/bin/env python
"""
Copyright 2020 Johns Hopkins University (Author: Jesus Villalba)
Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
import sys
import os
import argparse
import time
import logging
import numpy as np
import torch
import torch.nn as nn
from hyperion.hyp_defs import config_logger,... | |
"""classicML的核函数."""
import numpy as np
__version__ = 'backend.python.kernels.0.10.b0'
class Kernel(object):
"""核函数的基类.
Attributes:
name: str, default='kernel',
核函数名称.
Raises:
NotImplementedError: __call__方法需要用户实现.
"""
def __init__(self, name='kernel'):
"""
... | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from . import Dataset
import numpy as np
class MiniBatches(Dataset):
"""
Convert data into mini-batches.
"""
def __init__(self, dataset, batch_size=20, cache=True):
self.origin = dataset
self.size = batch_size
self._cached_train_s... | |
import os, sys
import numpy as np
import shutil
from tqdm import tqdm
from data_index import cat_id_to_desc, cat_desc_to_id, get_example_ids
sys.path.append('..')
from render_utils import render_obj_grid, render_obj_with_view
def render_example(example_id, render_dir, input_dir, output_dir, texture_dir, csv_file, sh... | |
import unittest
from cupy import _core
from cupy import testing
@testing.gpu
class TestArrayOwndata(unittest.TestCase):
def setUp(self):
self.a = _core.ndarray(())
def test_original_array(self):
assert self.a.flags.owndata is True
def test_view_array(self):
v = self.a.view()
... | |
import os
os.system('pip install -q efficientnet --quiet')
import tensorflow as tf
import pandas as pd
import numpy as np
import cv2
import itertools
from tensorflow.keras.applications.imagenet_utils import preprocess_input
class DataGenerator(tf.keras.utils.Sequence):
def __init__(self, dataset, batch_size,... | |
# -*- coding:UTF-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
import numpy as np
from math import floor
from .spp import *
from params import Args
import sys
import platform
if platform.python_version().split('.')[0] == '2':
sys.path.append('./')
from... | |
#!/usr/bin/python3
import os
import copy
import mmh3
import numpy as np
import math
from random import shuffle
from pathlib import Path
import json
from predictor.utility import msg2log
class BF():
"""
Bloom filter
For simplicity, bit array is replaced by bool array
"""
def __init__(self, filt... | |
# Copyright 2021 cms.rendner (Daniel Schmidt)
#
# 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... | |
"""
Tests for workspace module
"""
import os
import shutil
import tempfile
from six import StringIO
import numpy as np
import pytest
from fsl.data.image import Image
from oxasl import Workspace, AslImage
from oxasl.workspace import text_to_matrix
def test_default_attr():
""" Check attributes are None by default... | |
# Copyright 2022 IBM Inc. All rights reserved
# SPDX-License-Identifier: Apache2.0
# 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 ... | |
# SAve a file with 2*(n-1) columns contaning the (n-1) independent variables and the (n-1) gradients of the trained NN with respect these variables
import matplotlib.pyplot as plt
import numpy as np
import copy
import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
is_cuda = torch.cuda... | |
import torch
import numpy as np
import random
import collections
from sklearn.cluster import KMeans
from sklearn import metrics
import argparse
from toolbox import load_pickle, LR_classifier, shift_operator, eigenvalues, global_ratio
from toolbox import sample_case, l2_norm, compute_confidence_interval, diffused
def... | |
# -*- coding: utf-8 -*-
"""
@author: Adam Reinhold Von Fisher - https://www.linkedin.com/in/adamrvfisher/
"""
#This is part of a multithreading tool to speed up brute force optimization
#Import modules
from numba import jit
#Decorator
@jit
#Define function
def multithreadADXStratOpt():
#Import modules
impor... | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from datetime import timedelta
from .geo import shoot, gc_distance
# STORM TRACK LIBRARY
# TODO descatalogar?
def track_from_parameters(
pmin, vmean, delta, gamma,
x0, y0, x1, R,
date_ini, hours,
great_circle=Fals... | |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 24 08:20:07 2018
@author: Andrija Master
"""
import time
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from sklearn.metrics import roc_auc_score
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error... | |
import os
import cv2
import tensorflow as tf
slim = tf.contrib.slim
import sys
sys.path.append('slim')
import matplotlib.pyplot as plt
import numpy as np
from nets import inception
import tensorflow.contrib.slim.nets as nets
from preprocessing import inception_preprocessing
from lime import lime_image
import time
o... | |
""""""
"""
Copyright (c) 2021 Olivier Sprangers as part of Airlab Amsterdam
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 ... | |
import os
import random
from typing import Optional
import numpy
import torch
from scipy.stats import qmc
def set_seed(seed: Optional[int] = None):
# ref: https://www.kaggle.com/lars123/neural-tangent-kernel-2
if seed is None:
return
random.seed(seed)
os.environ["PYTHONASSEED"] = str(seed)
... | |
import numpy as np
import spams
import sys, getopt
import pandas as pd
from dl_simulation import *
from signature_genes import get_signature_genes, build_signature_model
from analyze_predictions import *
# from union_of_transforms import random_submatrix, double_sparse_nmf, smaf
from union_of_transforms import random_... | |
"""
Functions for evaluating forecasts.
"""
import numpy as np
import xarray as xr
#import properscoring as ps
import xskillscore as xs
import tqdm
from tqdm import tqdm
def load_test_data(path, var, years=slice('2017', '2018'), cmip=False):
"""
Args:
path: Path to nc files
var: variable. Geopo... | |
import torch
import numpy as np
from . import Kernel, Parameter, config
class LinearKernel(Kernel):
def __init__(self, input_dims=None, active_dims=None, name="Linear"):
super().__init__(input_dims, active_dims, name)
constant = torch.rand(1)
self.constant = Parameter(constant, lower=0.0)... | |
import sys
#sys.path.append('/export/zimmerman/khoidang/pyGSM')
sys.path.insert(0,'/home/caldaz/module/pyGSM')
from dlc import *
from pytc import *
from de_gsm import *
import numpy as np
states = [(1,0),(1,1)]
charge=0
filepath1 = 'scratch/tw_pyr_meci.xyz'
filepath2 = 'scratch/et_meci.xyz'
nocc=7
nactive=2
mol1 = ... | |
from unittest import TestCase
import numpy as np
from aspire.utils.coor_trans import grid_2d, grid_3d
from aspire.utils.matrix import roll_dim, unroll_dim, im_to_vec, vec_to_im, vol_to_vec, vec_to_vol, \
vecmat_to_volmat, volmat_to_vecmat, mat_to_vec, symmat_to_vec_iso, vec_to_symmat, vec_to_symmat_iso
import os.... | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# convert transitscore txt grid file - transit_score_israelyyyymmdd.txt - to an array of transitscore from 1-100
# then convert the array to a raster
# output ts_rendered _israelyyyymmdd.png
#
print('----------------- generate raster from grid file----------------------... | |
import numpy as np
import matplotlib.pyplot as plt
from transforms3d.euler import mat2euler
from scipy.linalg import expm
def load_data(file_name):
'''
function to read visual features, IMU measurements and calibration parameters
Input:
file_name: the input data file. Should look like "XXX_sync_KLT.npz"
... | |
from pymysql.converters import encoders as convertors
import numpy as np
""" Those methods extends pymysql convertor for numpy datatypes """
def convert_numpy_int(value, mapping=None):
return str(value)
def convert_numpy_float(value, mapping=None):
s = repr(value)
if s in ('inf', 'nan'):
raise ... | |
import cv2,selectivesearch
import numpy as np
import Intersection as union
candidates = set()
while True:
image = cv2.imread('image/dog.337.jpg')
img_lbl, regions = selectivesearch.selective_search(image)
for r in regions:
candidates.add(r['rect'])
for x,y,w,h in candidates:
iou = unio... | |
#!/usr/bin/env python
import argparse
import os
import scipy.constants as sc
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("dir", help="the file name to read")
args = parser.parse_args()
band_raw_dir = os.getenv("HOME") + "/mlp-Fe/input/Fe/band_data/raw_data/"
... | |
import numpy as np
import codecs, json
from json import JSONEncoder
class Numpy2JSONEncoder(json.JSONEncoder):
'''
This class is to convert the Numpy format Tensorflow Model Weights into JSON format
to send it to the server for Federated Averaging
'''
def default(self, obj):
if isinstance(... | |
import numpy as N
import os
def edog_abc_ext2(
p,
channel_dim,
patch_dim,
x):
xc = x % channel_dim
px = xc % patch_dim
py = xc / patch_dim
'''params: 0: cmu_x 1: cmu_y
2: csigma_x 3: csigma_y 4: ctheta
5: ccdir_a 6: ccdir_b 7: ccdir_c
8: smu_x 9: smu_y
10: ssigma_x 11: ssigma_... | |
import numpy as np
import numpy.random as npr
def OPV(S0, K, r, T, option_type):
M = 50
I = 10000
sigma = 0.25
def standard_normal_dist(M, I, anti_paths=True, mo_match=True):
if anti_paths is True:
sn = npr.standard_normal((M + 1, int(I / 2)))
sn = np.concatenate((sn... | |
import os
import sqlite3
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from flask import Flask
from flask import render_template
from flask import Response
import io
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
con = sql... | |
"""
Adapted from OpenAI Baselines
https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py
"""
from collections import deque
import numpy as np
import gym
import copy
import cv2
cv2.ocl.setUseOpenCL(False)
def make_env(env, stack_frames = True, episodic_life = True, clip_rewards = False, sca... | |
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
df1=pd.read_csv('stopwords')
b=[]
def func(df1):
for i in df1['words']:
b.append(i)
func(df1)
import string
df = pd.read_csv('emails.csv')
def func(a):
t=a.split(':')[1]
t.lstrip()
a=t
return a... | |
################################ΔΗΛΩΣΕΙΣ ΒΙΒΛΙΟΘΗΚΩΝ#####################################################################
import io
import sys
import time
from typing import List
import uvicorn
from fastapi import FastAPI, File, HTTPException, UploadFile
from PIL import Image
# Κάθε μοντέλο μηχανικής μάθησης φορτώνετ... | |
from tflearn.data_augmentation import ImageAugmentation
from tflearn.data_preprocessing import ImagePreprocessing
# import glob
# from sklearn import svm
from sklearn.ensemble import BaggingClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
# from itertools import compress
import ... | |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import os
project_name = "reco-tut-mlh"; branch = "main"; account = "sparsh-ai"
project_path = os.path.join('/content', project_name)
# In[2]:
if not os.path.exists(project_path):
get_ipython().system(u'cp /content/drive/MyDrive/mykeys.py /content')
import m... | |
import numpy as np
##from sklearn import hmm
from scipy.stats import norm
import nwalign as nw
from collections import defaultdict
import itertools
import time
from model_tools import get_stored_model, read_model_f5, read_model_tsv
onemers = [''.join(e) for e in itertools.product("ACGT")]
dimers = [''.join(e) for e in ... | |
# -*- coding: utf-8 -*-
# Author: Tonio Teran <tonio@stateoftheart.ai>
# Author: Hugo Ochoa <hugo@stateoftheart.ai>
# Copyright: Stateoftheart AI PBC 2021.
'''Unit testing the Keras wrapper.'''
import os
import unittest
import numpy as np
import inspect
from tensorflow.python.keras.engine.functional import Functional
... | |
#!/usr/bin/python
"""
Skeleton code for k-means clustering mini-project.
"""
import pickle
import numpy
import matplotlib.pyplot as plt
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
def Draw(pred, features, poi, mark_poi=False, name="image.png", f1_n... | |
"""
Stores utilities for use with lg.py and methods.py
"""
import csv
import numpy as np
import random
from tabulate import tabulate
import matplotlib.pyplot as plt
def testresultsfiletotable(testDataFile, transitionMatrixFile='', csvName=True):
"""
Takes a CSV file name as input and returns a usable Python di... | |
# MINLP written by GAMS Convert at 05/15/20 00:50:47
#
# Equation counts
# Total E G L N X C B
# 43 7 8 28 0 0 0 0
#
# Variable counts
# x b i s1s s2s sc ... | |
import cv2
import numpy as np
def preprocess(img, input_size, swap=(2, 0, 1)):
if len(img.shape) == 3:
padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
else:
padded_img = np.ones(input_size, dtype=np.uint8) * 114
r = min(input_size[0] / img.shape[0], input_siz... | |
from camas_gym.envs.camas_zoo_masking import MOVES, CamasZooEnv
import numpy as np
def update_batch_pre(env, done): # buffer may not be the correct terminology
"""Creates pre transition buffer data
Only one agent may act a time, other agents either carry out their current action again
or choose N... | |
import os
import argparse
import cv2
import numpy as np
import glob
import math
from objloader_simple import *
dir_markers = os.path.join(os.pardir,'markers')
dir_chess = os.path.join(os.pardir,'chessboards')
dir_objects = os.path.join(os.pardir,'objects')
MIN_MATCHES = 30
def capture_boards():
vd = cv2.VideoCap... | |
import neuromodulation.selection_functions as sf
import numpy as np
'''
Functions which are used to select the models which pass the criteria
New functions can be added, the function should accept criteria, voltages and shoudl return a dictionary,
which contain at least boolean key, which returns True or False pass ... | |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# 安装pandas
# pip install Pandas
# 运行测试套件
# 运行前需要安装: hypothesis和pytest
import pandas as pd
# pd.test()
# In[5]:
# 对象创建
# 传入一些值的列表来创建一个Series,pandas会自动创建一个默认的整数索引.
import pandas as pd
import numpy as np
import pprint
s = pd.Series([1,3,5,np.nan,6,8])
print(s)
pri... | |
from fitstools import manage_dtype, mask_fits, assign_header
from astropy.io import fits
import numpy as np
import matplotlib.pyplot as plt
import cosmics
def calibrate(image, bias, fiber_mask=None, lacosmic=True):
image = bias_correct(image, bias, fiber_mask)
image = dark_correct(image)
image = mask_badp... | |
import cv2
import os
import numpy as np
imgpath="./data_dir_crop_y/"
outputpath="./data_dir_crop_x/"
if not os.path.exists(outputpath):
os.makedirs(outputpath)
sum =1
for imgx in os.listdir(imgpath):
a, b = os.path.splitext(imgx)
img = cv2.imread(imgpath + a +b)
img = cv2.resize(img, (50, 50), interpolation=c... | |
from typing import Dict, List
import numpy as np
from stl_rules.stl_rule import STLRule
class ComfortLongitudinalJerk(STLRule):
"""
This rule implement a Comfort requirement on Longitudinal Jerk.
It is based on formalization reported in 5.2.22 of [3: Westhofen et al., 2021].
"""
@property
... | |
import os
import sys
import json
import subprocess
import numpy as np
from PIL import Image, ImageDraw, ImageFont
if __name__ == '__main__':
result_json_path = sys.argv[1]
video_root_path = sys.argv[2]
dst_directory_path = sys.argv[3]
if not os.path.exists(dst_directory_path):
subprocess.call(... | |
#!/usr/bin/env python
# Copyright 2014-2019 The PySCF Developers. 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
#
# U... | |
import networkx as nx
from py2neo import Graph, Node, Relationship
import pandas as pd
import random
from neo4j import GraphDatabase, basic_auth
import matplotlib
graph = Graph("bolt://localhost:7687", auth=("neo4j", "Password"))
driver = GraphDatabase.driver('bolt://localhost',auth=basic_auth("neo4j", "Password"))
db... | |
import pandas as pd
import seaborn as sb
import numpy as np
import matplotlib.pyplot as plt
from IPython import embed
##################### ATTENTION WEIGHTS PLOTTING #######################
class AttentionPlotter(object):
@classmethod
def plot(cls, weights, srcseq=None, dstseq=None, cmap="Greys", scale=1.):... | |
import numpy
from aydin.analysis.camera_simulation import simulate_camera_image
from aydin.io.datasets import characters
from aydin.it.transforms.variance_stabilisation import VarianceStabilisationTransform
def demo_vst():
image = characters()
image = image.astype(numpy.float32) * 0.1
noisy = simulate_c... | |
import numpy as np
import quaternion
from tbase.shader import Shader
from tbase import utils
from tbase.utils import Quaternion
try:
from pyglet.gl import *
except:
print("WARNING: pyglet cannot be imported but might be required for visualization.")
VERTEX_SHADER = ['''
varying vec3 normal, lightDir0, lightDi... | |
"""
Line Chart with Points
----------------------
This chart shows a simple line chart with points marking each value.
"""
# category: line charts
import altair as alt
import numpy as np
import pandas as pd
x = np.arange(100)
source = pd.DataFrame({
'x': x,
'f(x)': np.sin(x / 5)
})
alt.Chart(source).mark_line(poi... | |
"""Test file to visualize detected trail lines from videos"""
# Usage --> python Trackviz.py 3
# 0 --> Amtala
# 1 --> Bamoner
# 2 --> Diamond
# 3 --> Fotepore
# 4 --> Gangasagar
import cv2
import json
import math
import time
import sys
import matplotlib.pyplot as plt
from matplotlib import style
i... | |
import numpy as np
import matplotlib.pyplot as plt
import datetime
import glob2
import xarray as xr
import pandas as pd
#plt.close("all")
pd.options.display.max_columns = None
pd.options.display.max_rows = None
dircInput1 = 'C:/Users/Chenxi/OneDrive/phd/age_and_fire/data/02_semi_raw/07_ACE_FTS_with_AGEparams/'
dircIn... | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from . import thops
from . import modules
from . import utils
from models.transformer import BasicTransformerModelCausal
def nan_throw(tensor, name="tensor"):
stop = False
if ((tensor!=tensor).an... | |
import numpy as np
import os, errno, json, random
import torch
from rdkit import Chem, DataStructs
from rdkit.DataStructs import *
from katanaHLS.models import SimGNNConfig
try:
from descriptastorus.descriptors import rdDescriptors, rdNormalizedDescriptors
except:
raise ImportError("Please install pip install git+... | |
from sklearn.model_selection import KFold
from code.classification.classifier import Classifier
from code.classification.file import get_training_data
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import f1_score
from... | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import ctypes
import numpy
from nidaqmx._lib import (
lib_importer, wrapped_ndpointer, ctypes_byte_str, c_bool32)
from nidaqmx.system.physical_channel import Physica... | |
# -*- coding: utf-8 -*-
# tomolab
# Michele Scipioni
# Harvard University, Martinos Center for Biomedical Imaging
# University of Pisa
# Import an interfile volume as an Image3D and export.
from ...Transformation.Transformations import Transform_Scale
from ...DataSources.FileSources.interfile import load_interfile
... | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 4 20:39:07 2020
@author: JianyuanZhai
"""
import pyomo.environ as pe
import numpy as np
import time
DOUBLE = np.float64
class DDCU_Nonuniform():
def __init__(self, intercept = True):
self.intercept = intercept
self.ddcu = DDCU_... | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from datetime import datetime
try:
import numpy as np
except ImportError as e:
print("Failed to do 'from scipy.interpolate import interp1d', "
"scipy may not been installed properly: %s" % e)
try:
from scipy.interpolate import interp1d
except ImportE... | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 9 17:41:59 2020
@author: ullaheede
"""
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
import xarray as xr
import xesmf as xe
import pandas as pd
import glob as glob
import os
from pylab import *
import matplotlib.gr... | |
'''
ViZDoom wrapper
'''
from __future__ import print_function
import sys
import os
vizdoom_path = 'C://Users//Rzhang//Anaconda3//envs//recognition//Lib//site-packages//vizdoom'
sys.path = [os.path.join(vizdoom_path,'bin/python3')] + sys.path
import vizdoom
print(vizdoom.__file__)
import random
import time
import num... | |
import numpy as np
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import pdist
X=np.array([[1,2],[2,1],[3,4],[4,3]])
Z=linkage(X,'ward')
dendrogram(Z)
plt.show() | |
"""
helper functions for Helmsman
"""
# system packages
from __future__ import print_function
import os
import sys
import warnings
import itertools
import collections
import csv
from joblib import Parallel, delayed
from logging import StreamHandler, getLogger as realGetLogger, Formatter
from colorama import Fore, Back... | |
#!/usr/bin/env python3
# Copyright 2018 Lael D. Barlow
#
# 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 ... | |
import pytest
import numpy as np
from deduplipy.string_metrics.string_metrics import (length_adjustment, adjusted_ratio, adjusted_token_sort_ratio,
adjusted_token_set_ratio, adjusted_partial_ratio)
def test_length_adjustment():
assert length_adjustment('', '')... | |
# coding: utf-8
# In[15]:
from matplotlib import pyplot as plt
import numpy as np
#get_ipython().run_line_magic('matplotlib', 'inline')
x_old, x_new, gamma, prec = 0, 6, 0.01, 0.00001
f = lambda x: x**4 - 3 * x**3 + 2
df = lambda x: 4*(x**3) - 9*(x**2)
to_plot =[]
i = 0
to_plot.append(x_new)
while abs(x_new - x... | |
"""
* Program to practice with OpenCV drawing methods.
"""
import skimage.io
import numpy as np
import random
# create the black canvas
image = np.zeros(shape=(600, 800, 3), dtype="uint8")
# WRITE YOUR CODE TO DRAW ON THE IMAGE HERE
# display the results
skimage.io.imshow(image) | |
"""
RegionFile class.
Reads and writes chunks to *.mcr* (Minecraft Region)
and *.mca* (Minecraft Anvil Region) files
"""
from __future__ import absolute_import, division
import logging
import os
import struct
import zlib
import time
import numpy
from mceditlib import nbt
from mceditlib.exceptions import ... | |
# Authors: Stephane Gaiffas <stephane.gaiffas@gmail.com>
# License: BSD 3 clause
"""
Comparisons of decision functions
=================================
This example allows to compare the decision functions of several random forest types
of estimators. The following classifiers are used:
- **AMF** stands for `AMFClas... | |
import numpy as np
from numpy.random import default_rng, Generator
from .metric import accuracy
from ..classification import KNNClassifier
from ml_utils import classification
def k_fold_split(n_splits: int, n_instances: int, rng: Generator = default_rng()) -> list:
""" Split n_instances into n mutually exclusive ... | |
"""
DCG and NDCG.
TODO: better docs
"""
import numpy as np
from . import gains, Metric
from six import moves
_EPS = np.finfo(np.float64).eps
range = moves.range
class DCG(Metric):
def __init__(self, k=10, gain_type='exp2'):
super(DCG, self).__init__()
self.k = k
self.gain_type = gain_t... | |
"""The classes in this file are domain specific, and therefore include
specifics about the design space and the model parameters.
The main jobs of the model classes are:
a) define priors over parameters - as scipy distribution objects
b) implement the `predictive_y` method. You can add
whatever useful helper functi... | |
import os
import io
import gzip
import pickle
import tarfile
import logging
import torch
import numpy as np
import utils
from model import embedding
def add_embed_arguments(parser, name=None):
if name is None:
prefix = ""
else:
prefix = f"{name}-"
parser.add_argument(f"--{prefix}embed-ty... | |
import copy
from typing import Union, List, Callable
import numpy as np
from interpreter import imageFunctions as imageWrapper
from interpreter import lexer as lexer
from interpreter import tokens as tokens
from interpreter import movementFunctions as movement
from interpreter import colors as colors
from interpreter... | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
""""WRITEME"""
import numpy as np
import matplotlib.pyplot as plt
import pygimli as pg
def drawFirstPicks(ax, data, tt=None, plotva=False, marker='x-'):
"""Naming convention. drawFOO(ax, ... )"""
return plotFirstPicks(ax=ax, data=data, tt=tt,
... | |
"""Utility functions for real-space grid properties
"""
import numpy as np
import matplotlib
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
import struct
from .conversions import *
from scipy.interpolate import griddata
rho = np.zeros(2)
rho_val = np.zeros(2)
unitcell = np.zeros(2)
grid... | |
#Automatic_keyboard_recognition
import numpy as np
import math
import statistics as stats
import cv2 as cv2
class Keyboard_auto_find_and_transform:
def __init__(self,initial_frame,target_dimensions):
print("Finding Keyboard")
self.target_dimensions = target_dimensions
self.p_mat = Automatic_keyboard_r... | |
"""
NLTK Word Frequency Summarization
Modified : Shashank
Original author: Akash P
"""
import nltk
import hashlib
import numpy as np
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize, sent_tokenize
from hashlib import sha224
from SummarizationInterface impo... | |
import numpy as np
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
from numpy.core.fromnumeric import prod
from .autoencoder import utils
from .autoencoder.moco import builder
from .autoencoder.moco import loader
from .autoencoder.model_ae_moco import AutoEncoder
from .d... | |
# -*- coding: utf-8 -*-
"""test_resultsreconstruction
Tests that a single depletion step is carried out properly.
The entire sequence from cross section generation to depletion execution is
tested. Results are compared against pre-generated data using a different code.
Created on Thu Oct 28 08:59:44 2021 @author: Mat... | |
#!/usr/bin/python
import glob
import math
import os
import shutil
import struct
import sys
import csv
import Queue
import thread
import subprocess
from optparse import OptionParser
from osgeo import gdalconst
from osgeo import gdal
from osgeo import osr
from numpy import *
import numpy as np
import utilities
def... | |
import os, sys
import cv2
import numpy as np
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from qgis.gui import QgsMapCanvas, QgsMapToolPan, QgsMapToolZoom, QgsMapToolIdentify
from qgis.core import QgsProject, QgsApplication, QgsVectorLayer, QgsRasterLayer
from Dlg_unsupervi... | |
import numpy as np
from collections import defaultdict
def pairwise_view(target_station, next_station, mismatch='error'):
if target_station is None or next_station is None:
return ValueError("The data is empty.")
if target_station.shape != next_station.shape:
return None # ValueError("Paired ... | |
# import the necessary packages
from imutils.video import VideoStream
from imutils import face_utils
import imutils
import time
import dlib
import cv2
import numpy as np
import math
import transformation
import utils
# custom imports
import rotation_matrix_util as rmu
import client
def handle_drone_directions(img, r... | |
import random
import cv2
import matplotlib.pyplot as plt
import numpy as np
from word_segmentation import extract_words_from_image
def word_image_preprocess(img, imgSize=(128, 32), dataAugmentation=False):
"""put img into target img of size imgSize, transpose for TF and normalize gray-values"""
# there are... | |
#!/usr/bin/env python3
import itertools
import os.path
import pickle
from typing import Any, Generator, Hashable, Iterable, NamedTuple, Sequence, Tuple
import numpy as np
from rosplane_msgs.msg import State, Current_Path
from rosbag_to_traces import process_bag_file, dist_trace_to_mode_seg_tuples, aggregate_by_mode... | |
import numpy as np
np.random.seed(2591)
class DAGANDataset(object):
def __init__(self, batch_size, last_training_class_index, reverse_channels, num_of_gpus, gen_batches):
"""
:param batch_size: The batch size to use for the data loader
:param last_training_class_index: The final index for ... | |
import unittest
import neuralnetsim
import networkx as nx
class TestNetworkAnalysis(unittest.TestCase):
def test_calc_mu(self):
graph = nx.DiGraph()
graph.add_node(1, com=1)
graph.add_node(2, com=1)
graph.add_node(4, com=2)
graph.add_node(5, com=3)
graph.add_edge(1,... | |
from dipsim import multiframe, util, detector, illuminator, microscope, util
import numpy as np
import matplotlib.pyplot as plt
import os; import time; start = time.time(); print('Running...')
# Main input parameters
n_pts = 1000
ill_types = ['unpolarized', 'unpolarized', 'wide']
ill_pols = [0, np.pi/4, np.pi/2]
det_... | |
from base import BaseDataSet, BaseDataLoader
from utils import pallete
import numpy as np
import os
import scipy
import torch
from PIL import Image
import cv2
from torch.utils.data import Dataset
from torchvision import transforms
import json
class CUS_Dataset(BaseDataSet):
def __init__(self, **kwargs):
se... | |
from sklearn.linear_model import Lasso
import numpy as np
def norm_entropy(p):
n = p.shape[0]
return -p.dot(np.log(p + 1e-12) / np.log(n + 1e-12))
def entropic_scores(r):
r = np.abs(r)
ps = r / np.sum(r, axis=0)
hs = [1 - norm_entropy(p) for p in ps.T]
return hs
def nrmse(predicted, target... | |
# --------------------------------------------------------
# Written by Yufei Ye (https://github.com/JudyYe), modified by Zhiqiu Lin (zl279@cornell.edu)
# --------------------------------------------------------
from __future__ import print_function
import argparse
import os
import os.path as osp
import numpy as np
f... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.