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<filename>mvlearn/decomposition/ajive.py
# MIT License
# Copyright (c) [2017] [<NAME>]
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... |
__author__ = "<NAME>"
import unittest
from copy import deepcopy
from random import uniform
from typing import List, Union
from numpy import asarray, eye, ndarray
from scipy.stats import unitary_group
from theboss.boson_sampling_simulator import BosonSamplingSimulator
from theboss.boson_sampling_utilities.permanent_c... |
<filename>data_utility.py
import os
import glob
import numpy as np
from scipy import misc
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import random
class ToTensor(object):
def __call__(self, sample):
return... |
import numpy
import os
import nltk
import pdb
import time
import skipthoughts
import cPickle
from training import tools
from scipy.io import loadmat
def preprocess(line, vocab):
'''Perform the same kind of preprocessing as in the IMDB experiments.'''
tokens = nltk.word_tokenize(line)
tokenized_sentence = ... |
"""charcnn.py: classes that might be used when modeling classes.
<NAME> <<EMAIL>>, 2020. No rights reserved.
# C0 1.0 Universal
_Official translations of this legal tool are available [in other languages](
https://creativecommons.org/publicdomain/zero/1.0/legalcode)._
CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM... |
<filename>IPythonWorkflow/uniformgradientboosting.py
from __future__ import print_function
from __future__ import division
from collections import defaultdict, OrderedDict
from itertools import izip
import numbers
from time import time
import itertools
import math
import scipy.sparse as sparse
import sklearn
from skle... |
<reponame>tesserato/envelope<filename>06graph.py
import numpy as np
import signal_envelope as se
from scipy import interpolate
import plotly.graph_objects as go
for file in listdir("test_samples"):
name = file.split(".")[0]
print(name)
W, _ = se.read_wav(f"test_samples/{name}.wav")
amp = np.max(np.abs(W))
... |
<reponame>ManMohan291/PyProgram<gh_stars>1-10
from os import system
import numpy as np
import scipy.optimize as op
import matplotlib.pyplot as plt
####################################################################
def getPlot():
return plt
####################################################################
def... |
<reponame>Integrative-Transcriptomics/VIPurPCA
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import rcParams
from sklearn.metrics.pairwise import euclidean_distances
import pandas as pd
import numpy as np
#from Animation import gs
import matplotlib
from generate_samples import easy_example_data_... |
<reponame>bthyreau/hippodeep
from __future__ import print_function
from lasagne.layers import get_output, InputLayer, DenseLayer, ReshapeLayer, NonlinearityLayer
from lasagne.nonlinearities import rectify, leaky_rectify, elu
import sys, os, time
import nibabel
import numpy as np
import scipy.ndimage
import theano
imp... |
<gh_stars>0
import os
import glob
import random
import math
import numpy as np
from tqdm import tqdm
from scipy.io import loadmat
class ACCORDDataset(object):
def __init__(self, data_dir, fault_flags, ab_range, time_steps, channels, k, use_normal):
self.data_dir = data_dir # CWRUデータフォルダ
self.fault_... |
import numpy as np
import matplotlib.pyplot as mpl
from scipy.optimize import fmin_powell as fmin
from scipy.odr import *
from scipy.stats import chi2
t = np.asfarray([ 352 , 701 , 1048 , 1398 , 1751 , 2099 , 2446 , 2805 ])
t_err = np.asfarray([ 5 , 9 , 9 , 9 , 9 , 15 , 15 , 15 ])
dT = np.asfarray([ 10.0 , 19.7 ... |
# tutorial on how to generate Poisson random numbers using the "direct method"
# if we have a sequence of random numbers E following an exp. distribution lam * exp(-x/lam)
# than the integer values where the cumulative sum \sum_{k=1}^n E_k > 1 follows a Poisson
# distribution (homogeneous Poisson point process)
import... |
<gh_stars>0
"""Contains code basic implementation of GAN modeling a Gaussian.
- Improvement on version 1.
- Better choice of latent variables.
- Better learning rate decay.
"""
from pprint import pprint
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import scipy as sp
import seaborn as sn... |
"""
total generalized variation zooming to demonstrate the cuda implementation
You have to install pycuda and the cuda toolchain to be able to use the pycuda
implementation.
If you get errors during cuda kernel compilation, you might have to set the
environment variable PYCUDA_DEFAULT_NVCC_FLAGS to an appropriate v... |
import os
import time
import numpy as np
import tensorflow as tf
from scipy.misc import imread, imresize, imsave
from tensorflow.contrib import rnn
from data_manager import DataManager
from utils import (
sparse_tuple_from,
resize_image,
label_to_array,
ground_truth_to_word,
levenshtein,
)
os.envi... |
<filename>gbra/attackers/attacker.py<gh_stars>1-10
import random
import numpy as np
from scipy import stats
import snap
from abc import abstractmethod
from collections import Counter
from gbra.recommender.recommenders import PixieRandomWalkRecommender
class BaseAttacker(object):
"""Base configurations for an Atta... |
import numpy as np
import numpy.fft as fft
import mrcfile
from scipy import ndimage
from scipy.interpolate import interp1d
import scipy.stats as st
def read_mrc(file):
with mrcfile.open(file, permissive=True) as m:
return m.data.astype(np.float32)
def resize(arr, target_shape, **kwargs):
idx = []
... |
"""
Utility functions for the multiway matching
"""
import numpy as np
import scipy.optimize as sco
def u_projector(v, sizes):
"""
Projections over a set of permutation (for each graph)
:param v: the current approximation matrix
:param sizes: the sizes of the different graphs
:return: the project... |
from sympy import *
def test_sin():
x, y = symbols('xy')
k = Symbol('k', integer=True)
assert sin(nan) == nan
assert sin(oo*I) == oo*I
assert sin(-oo*I) == -oo*I
assert sin(0) == 0
assert sin(1) == sin(1)
assert sin(-1) == -sin(1)
assert sin(x) == sin(x)
assert sin(-x) ==... |
## Machine Learning Online Class
# Exercise 8 | Anomaly Detection and Collaborative Filtering
#
# Instructions
# ------------
#
# This file contains code that helps you get started on the
# exercise. You will need to complete the following functions:
#
# estimateGaussian.m
# selectThreshold.m
# cofiCos... |
import math
from scipy.stats import norm
class BSM:
def __init__(self, price, strike, interest_rate, volatility, time):
self.S = price
self.X = strike
self.r = interest_rate
self.v = volatility
self.T = time
@property
def d1(self):
d1_numerator = math.log(... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sympy import symbols
from sympy import limit
if __name__ == '__main__':
x = np.random.randn(1000)
y = np.arange(1001)
# plt.plot(x)
# plt.show()
print y
s = np.random.dirichlet((10, 5, 3), 20).transpo... |
import numpy as np
from scipy.interpolate import interp1d
import matplotlib
import matplotlib.pyplot as plt
from Make_Timelist import *
import sys
sys.path.insert(0, '/tera/phil/nchaparr/python')
import nchap_fun as nc
from matplotlib import rcParams
rcParams.update({'font.size': 10})
"""
Plots
"""
Fig1 = plt.f... |
<reponame>h-mayorquin/g_node_data_analysis_205
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal
from load_data import responses_strong, stimulus_strong
from math import floor
plot_firing_rate = True
plot_correlation = True
ratio = 0.1
size_to_use = int(ratio * stimulus_strong.size)
stimulus_stro... |
<filename>patch_trcking_full.py
# import the necessary packages
from shape import ShapeDetector
import argparse
import imutils
import cv2
import time
# construct the argument parse and parse the arguments
from imutils.video import VideoStream,FileVideoStream
import numpy as np
from scipy.spatial import distance
# cons... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import glob
import logging
import argparse
from collections import namedtuple
from scipy.stats import poisson
LEVEL = logging.INFO
LOGFILE, LOGFILEMODE = '', 'w'
HANDLERS = [logging.StreamHandler(sys.stdout)]
if LOGFILE:
HANDLERS.append(logging.... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
PSF Photometry.
.. codeauthor:: <NAME> <<EMAIL>>
"""
import os.path
import numpy as np
import logging
#from copy import deepcopy
from scipy.optimize import minimize
from . import BasePhotometry, STATUS
from .psf import PSF
from .utilities import mag2flux
from .plots i... |
<reponame>SkillsFundingAgency/das-data-management-ai
import pickle
import os
import pandas as pd
import numpy as np
import pyarrow.parquet as pq
import joblib
import datetime
import shap
import math
import xgboost as xgb
import matplotlib.pyplot as plt
from azureml.core.run import Run
from azureml.core import Dataset, ... |
"""Some sample datasets"""
from __future__ import division
import os
import numpy as np
from scipy import ndimage
from sklearn.utils import check_random_state
def get_megaman_image(factor=1):
"""Return an RGBA representation of the megaman icon"""
imfile = os.path.join(os.path.dirname(__file__), 'megaman.pn... |
<gh_stars>10-100
import tensorflow as tf
import os
import sys
import data_generation
import networks
import scipy.io as sio
import param
import util
import cv2
import truncated_vgg
from keras.backend.tensorflow_backend import set_session
from keras.optimizers import Adam
from PIL import Image
import numpy as np
from ht... |
<filename>bay_opt/acq.py<gh_stars>0
import numpy as np
from scipy.stats import norm
def lower_confidence_bound(X, Y, model, kappa=1.96):
mu, sigma = model.predict(X, return_std=True)
return mu - kappa * sigma
def upper_confidence_bound(X, Y, model, kappa=1.96):
mu, std = model.predict(X, return_std=True... |
<reponame>srodney/romansims
import os
import numpy as np
from matplotlib import pyplot as plt
from scipy import interpolate as scinterp
from datetime import datetime
from astropy.io import fits
from astropy.table import Table, Column, Row, MaskedColumn
from astropy import table
from astropy import units as u
import s... |
<reponame>Chenguang-Zhu/relancer
#!/usr/bin/env python
# coding: utf-8
# ## Sample End to End ML Project
#
# Note: made while learning with Chapter 2 of [Hands on ML Book](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) , official code at [Github](https://github.com/ageron/handson-ml/bl... |
<reponame>Conengmo/ovencontrol
import matplotlib.pyplot as plt
import control
import numpy as np
import scipy.linalg
class PID:
"""Discrete PID control"""
def __init__(self, Kp, Ki, Kd, u_max=None, u_min=None, integrator_max=None, integrator_min=None):
self.Kp = Kp
self.Ki = Ki
self.Kd... |
<gh_stars>1-10
from multiprocessing.pool import Pool
import datajoint as dj
from tqdm import tqdm
from ..utils.measures import corr
import numpy as np
from scipy import stats
from .parameters import DataConfig
from .data import MovieMultiDataset, MovieScan
from ..utils.logging import Messager
schema = dj.schema('n... |
<reponame>winsheng1996/fuzzy
####################################################################
#
# Pattern Search (PS) and Local Unimodal Sampling (LUS).
# Heuristic optimization methods for real-valued functions.
#
# This is a preliminary version that needs a few features before release.
#
# Reference:
#
# Tuning ... |
<reponame>OleBo/Stock-Prediction-Models
import numpy as np
import pandas as pd
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import cophenet
from scipy.spatial.distance import pdist
import pylab
# On 20151227 by MLdP <<EMAIL>>
# Hierarchical Risk Parity
def getIVP(cov... |
<gh_stars>0
import math
from statistics import mean
import random
import numpy as np
import pandas as pd
from copy import deepcopy
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.cluster import MeanShift
from sklearn.cluster import KMeans
from sklearn.linear_model im... |
import numpy as np
def chan_vese(im_input, im_mask, sigma,
dt=1.0, mu=0.2, lambda1=1, lambda2=1, iter=100):
"""Region-based level sets.
Region-based level set implementation based on the Chan-Vese method.
Provides cost terms for boundary length, the variance of intensities inside
the ze... |
from copy import deepcopy
import numpy as np
from scipy.stats import gamma, norm
from ... import constants
from ...constants import Version
from .base import BMDModel, DefaultParams
class Dichotomous(BMDModel):
dtype = constants.DICHOTOMOUS
possible_bmr = ("Extra", "Added")
def get_bmr_text(self):
... |
<reponame>FabianKaechele/Multivariate_Extension_Lorenz_Gini
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
import scipy.stats as st
from matplotlib.colors import LightSource
####--------------------------------####
# This is the corresponing Python code to the paper "A multi... |
#!/usr/bin/env python
from __future__ import print_function, division
import bayesloop as bl
import numpy as np
import sympy.stats as stats
class TestTwoParameterModel:
def test_step_set1TM_0hp(self):
# carry out fit
S = bl.OnlineStudy(storeHistory=True)
S.setOM(bl.om.Gaussian('mean', bl.... |
<reponame>Amihaeseisergiu/Advanced-Chapters-Of-Neural-Networks
import numpy as np, pickle, gzip, os
from numpy.random import normal as rnorm, uniform as runif, shuffle, binomial
from scipy import ndimage
from scipy.ndimage import rotate, shift
class Utility:
def read_data(path):
f = gzip.open(path, 'rb')
... |
# (C) <NAME> and the Qrack contributors 2017-2021. All rights reserved.
#
# Use of this source code is governed by an MIT-style license that can be
# found in the LICENSE file or at https://opensource.org/licenses/MIT.
import math, cmath
from ctypes import *
from .qrack_system import Qrack
from .pauli import Pauli
c... |
import sys
import numpy as np
from numpy.linalg import norm
import matplotlib.pyplot as plt
from sympy import symbols
sys.path.append('../')
from dmd import dmd
if __name__ == '__main__':
# Parameters
t_start = 0.0 # start simulation time
t_stop = 20.0 # stop simulations time
... |
<reponame>liyang0711/puffins
"""Functionality relating to statistics, timeseries, etc."""
from eofs.xarray import Eof
import numpy as np
import scipy.stats
import xarray as xr
from .names import LAT_STR, YEAR_STR
from .nb_utils import cosdeg
# Trends: computing trends, detrending, etc.
def trend(arr, dim, order=1, r... |
# -*- Mode: python
#
# Licenses
# Authors
"""\
Functions to handle openDX files
================================
Comments
"""
###########################################################################
#### imports #
###########################################################################
import numpy... |
<reponame>rcmalli/polimi-dl-project<filename>tools/create_fake_dataset.py<gh_stars>1-10
import numpy as np
import os
import pickle
from tqdm import tqdm
from scipy.ndimage import imread
image_folder = '../data/fake/image'
depthmap_folder = '../data/fake/depth'
data = {'image':[],'depth':[]}
image_paths = sorted([os.... |
<reponame>sekilas13/Python
from __future__ import annotations
from cmath import sqrt
def akar_kuadrat(a: int, b: int, c: int) -> tuple[complex, complex]:
"""
diberikan koefisien numerik a, b dan c,
menghitung akar untuk setiap persamaan kuadrat dalam bentuk ax^2 + bx + c
>>> akar_kuadrat(a = 1,b = ... |
import cPickle
import numpy as np
from scipy.io.wavfile import read
from sklearn.mixture import GaussianMixture
from featureextraction import extract_features
#from speakerfeatures import extract_features
import warnings
warnings.filterwarnings("ignore")
#path to training data
# source = "development_set/"
source ... |
<reponame>dennis-l/tolteca
#! /usr/bin/env python
# Author:
# <NAME>
#
# History:
# 2020/02/15 <NAME>:
# - First staged.
"""
This recipe makes use of the `KidsSimulator` to investigate
the total PSD noise as function of Qr and the readout noise.
"""
from astropy import units as u
from kidsproc.kidsmodel.... |
"""
Location–Scale Family Distribution Base Class
A location–scale family is a family of probability distributions
parametrized by a location parameter and a non-negative scale
parameter
"""
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Type, TypeVar
from jax import jit, scip... |
<reponame>EgorOrachyov/MachineLearning
import numpy as np
import pandas as pd
import random
import array
import csv
from sklearn.preprocessing import OneHotEncoder
from scipy.sparse import hstack
from scipy.sparse import vstack
from sklearn.utils import shuffle
class Model:
def __init__(self, n, k):
self... |
<filename>tests/cupyx_tests/scipy_tests/fft_tests/test_realtransforms.py<gh_stars>0
import numpy as np
import pytest
from cupyx.scipy import fft as cp_fft
from cupy import testing
try:
# scipy.fft is available since scipy v1.4.0+
import scipy.fft as scipy_fft # noqa
except ImportError:
scipy_fft = None
... |
import unittest
import numpy as np
import scipy.sparse
from sklearn.datasets import load_boston, load_iris, load_wine
from flaml import AutoML
from flaml.data import get_output_from_log
from flaml.model import SKLearnEstimator
from rgf.sklearn import RGFClassifier, RGFRegressor
from flaml import tune
class MyRegul... |
<gh_stars>1-10
# Copyright 2020 Google LLC
#
# 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 ... |
from scipy.stats import chisquare
import scipy.stats as stats
import numpy as np
# keypoints
# nose-to-neck
# neck-to-rsho, rsho-to-relb, relb-to-rwrist
# neck-to-lsho, lsho-to-lelb, lelb-to-lwrist
# neck-to-midhip
# midhip-to-rhip, rhip-to-rknee, rknee-to-rankle
# midhip-to-lhip, lhip-to-lknee, lknee-to-lankle
man = ... |
<reponame>OOAmusat/idaes-pse
# -*- coding: utf-8 -*-
#################################################################################
# The Institute for the Design of Advanced Energy Systems Integrated Platform
# Framework (IDAES IP) was produced under the DOE Institute for the
# Design of Advanced Energy Systems (ID... |
"""
Evaluate hermite polynomials using cuda
* use modified version of previously written polynomial evaluator
"""
import warnings
warnings.simplefilter('ignore',Warning)
import numpy
import time
from math import sqrt, log10, exp, ceil
import sys
from optparse import OptionParser
from scipy.special import hermite
im... |
import sys
import os
import jieba
import logging
import math
import gensim
import numpy as np
from scipy.spatial import distance
from pathlib import Path
def sentence_similarity(input1, input2, model = None):
# Word2Vec Cosine distance
if len(input1) == 0 or len(input2) == 0:
if len(input1) == 0:
... |
<gh_stars>1-10
# coding: utf-8
# public items
__all__ = [
"allan_variance",
"copy_function",
"deprecation_warning",
"one_thread_per_process",
"psd",
"slicewhere",
]
# standard library
from collections import OrderedDict
from contextlib import contextmanager
from functools import wraps
from lo... |
<reponame>maoshenl/StarSampler
import numpy as np
import random as rand
import scipy.optimize
import scipy.special as ss
import time
def impt_sample(model_class, steps, resample_factor, samplesize,
replace=True, r_vr_vt=False, r_v=False, filename=None):
'''
Sampling Importance Resamplin... |
<reponame>zhubaiyuan/learning-algorithms
import numpy as np
import timeit
from scipy.optimize import curve_fit
from common.table import DataTable
from common.modeling import numpy_error, log_model
def binary_array_search(nums, target):
"""
Use Binary Array Search to search for target in ordered list nums.
... |
import pdb
import numpy as np
from scipy import linalg, optimize
import cov
class GP(object):
def __init__(self,X,y,cov_type='covSEard'):
self.X = X # training inputs, 2d array
self.y = y # training outputs, 1d array
self.cov_type = cov_type # string specifying covariance function
... |
<filename>Files/pogo-bot.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# import the necessary packages
import cv2
import numpy as np
import imutils
#colors
from webcolors import rgb_to_name,CSS3_HEX_TO_NAMES,hex_to_rgb #python3 -m pip install webcolors
from scipy.spatial import KDTree
def convert_rgb_to_names(rgb_tu... |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modif... |
<reponame>rickyHong/MS-CNTK
# ==============================================================================
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE.md file in the project root
# for full license information.
# =======================================================... |
<reponame>jazminferreiro/simulacion<gh_stars>0
# -*- coding: utf-8 -*-
import datetime, time
class GeneradorLinealCongruente:
def __init__(self,a,c,m, inicial):
self.a = a
self.c = c
self.m= m
self.actual = inicial
def generar(self):
self.actual = (self.a*self.actual + ... |
import datetime
from datetime import timedelta
from statistics import mean
from typing import Dict
from typing import Optional
from typing import Tuple
from django.utils import timezone
from chat_wars_database.app.business_core.models import Item
from chat_wars_database.app.business_exchange.models import StatsByDay
... |
<filename>examples/double_pendulum/double_pendulum.py
# # from modeling.mechanics.dynamics import MechanicalSystem
from lib.symbolical_dynamics.euler_lagrange import MechanicalSystem
from sympy import Matrix, symbols, cos, sin, simplify, lambdify
from sympy.utilities.autowrap import autowrap
from sympy.utilities.codege... |
# coding=utf-8
# Copyright 2021 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
<filename>classifiers.py
from skimage.transform import hough_line, hough_line_peaks
from skimage.morphology import disk, binary_erosion
from skimage.measure import label, regionprops
import numpy as np
from scipy import stats
from skimage.draw import line
from itertools import groupby
import math
import cv2
from common... |
<gh_stars>1-10
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResBlock(nn.Module):
def __init__(self, channels, scale=0.1):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, 3, 1, padding=1)
self.conv2 = nn.Conv2d(channels, channels, 3, 1,... |
<filename>udacity/assigment1.py
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.linear_model import LogisticRegression
from six.moves.urllib.request import urlr... |
#!python3
from ut import p
from fractions import *
F = Fraction
p("Fraction")
fr1 = F('0.2')
print(fr1)
print(fr1.numerator)
print(fr1.denominator)
p("num/den")
fr1 = F(2, 10)
print(fr1)
print(fr1.numerator)
print(fr1.denominator)
p('from float')
print(Fraction.from_float(0.2)) |
import numpy as np
import scipy.sparse as sparse
import matplotlib.pyplot as plt
import matplotlib.colors as c
import matplotlib as mpl
import scipy.sparse.linalg as slinalg
import time as time
import cv2
# Default arguments:
# delta_x Numpy array of length 3 containing the size of the grid
# dx: Numpy... |
<filename>tabular/src/autogluon/tabular/models/tabular_nn/categorical_encoders.py
"""
Variant of the sklearn OneHotEncoder and OrdinalEncoder that can handle unknown classes at test-time
as well as binning of infrequent categories to limit the overall number of categories considered.
Unknown categories are returned a... |
<reponame>YiZhan2854/ppnp
import numpy as np
import scipy.sparse as sp
import tensorflow as tf
from .utils import sparse_matrix_to_tensor, sparse_dropout
sparse_dot = tf.sparse_tensor_dense_matmul
def calc_A_hat(adj_matrix: sp.spmatrix) -> sp.spmatrix:
nnodes = adj_matrix.shape[0]
A = adj_matrix + sp.eye(nn... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2020 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
#
# Un... |
<filename>scripts/sources/S_FlexibleCombinationsModels.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.4'
# jupytext_version: 1.1.4
# kernelspec:
# display_name: Python 3
... |
import numpy as np
import cvxpy as cvx
from scipy import sparse
from time import time
def project(W, **kw):
verbose = kw.get('verbose', False)
M = len(W)
V = cvx.Variable(M)
p = cvx.Problem(cvx.Minimize(cvx.norm2(V-W)), [cvx.sum_entries(V) == 1., V > 0.])
try:
p.solve(max_iters=100000, ver... |
<filename>skextremes/models/wind.py
"""
This module contains algorithms found in the literature and used extensively in
wind energy engineering as standard methods.
For more information visit:
https://www.ecn.nl/publications/ECN-C--98-096
https://webstore.iec.ch/preview/info_iec61400-1%7Bed3.0%7Den.pdf
"""
import n... |
import os
import pickle
import glob
import re
import pyedflib
import numpy as np
import scipy.signal as ssignal
from tqdm import tqdm
from sklearn import preprocessing
# AUTHOR: https://github.com/drasros/sleep_staging_shhs/blob/master/shhs.py
# NOTE: Does not work on SHHS2 yet...
# SHHS2 has a mix of 125Hz recor... |
import numpy as np
from numpy.linalg import norm
from scipy.sparse import issparse
import scipy.sparse.linalg as slinalg
from sparse_ho.forward import get_beta_jac_iterdiff
class Backward():
def __init__(self, criterion):
self.criterion = criterion
def get_beta_jac_v(
self, X, y, log_alph... |
<gh_stars>1-10
import scipy.io as sio
import scipy.stats as sps
import numpy as np
import teneto
import matplotlib.pyplot as plt
import pandas as pd
#import teneto.stats.shufflegroups as shuffle
plt.rcParams['image.cmap'] = 'gist_gray'
Eeo=np.zeros([46])
Eec=np.zeros([46])
for s in range(0,46):
print('Calculati... |
<filename>PyQuantum/TC_closed/Hamiltonian.py<gh_stars>0
# ---------------------------------------------------------------------------------------------------------------------
# system
# ---------------------------------------------------------------------------------------------------------------------
# scientific
im... |
<gh_stars>1-10
"""
Implementation of simple bandit and gradient bandit algorithm from chapter 2
of Sutton andBarto's "Reinforcement Learning"
"""
import numpy as np
from scipy.special import softmax
def simple_bandit_alg(MAB, epsilon, max_steps, alpha = 0, Q1 = 0,
UCB = False, c = 2, ru... |
"""
Test the ColumnTransformer.
"""
import numpy as np
from scipy import sparse
import pytest
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing i... |
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import scipy
from sklearn.cluster import KMeans
import AnchorSet
MAX_PAIRWISE_SCORE = 11 #based on blosum62, probably needs adjustment for other matrices
# based on an anchor set, defines a set of patterns ... |
<reponame>edu-gp/annotation_tool<gh_stars>1-10
import json
import os
import shlex
import subprocess
import numpy as np
import pandas as pd
from scipy.special import softmax
from sklearn.model_selection import train_test_split
BINARY_CLASSIFICATION = "binary"
MULTILABEL_CLASSIFICATION = "multilabel"
# TODO: Move loa... |
from pytexmex.core import *
import numpy as np, pandas as pd
import scipy.stats
import warnings
def read_otu_table(fn, transpose=False):
'''
Read an OTU table from a filename (or filehandle). It expects to
have OTUs on the rows and samples on the columns (i.e., QIIME style).
If the table is wider than ... |
import pysal as ps
import numpy as np
import scipy.spatial as ss
from random import shuffle
import itertools
#Build the lattices and save as a shapefile
lattices = [32,100,224,317]
for lattice in lattices:
ps.weights.build_lattice_shapefile(lattice,lattice,"{}.shp".format(lattice**2))
polys = [1000,10000,500... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, fcluster
from dtaidistance import dtw, dtw_visualisation, clustering
from dtaidistance import dtw_visualisation as dtwvis
from sklearn.preprocessing import RobustScaler, StandardScaler
import pickle
fr... |
<reponame>umarov90/DeepFake<filename>deepfake.py
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from tensorflow.python.keras import regularizers
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.laye... |
<filename>tensorflow_quantum/python/layers/high_level/noisy_pqc_test.py
# Copyright 2020 The TensorFlow Quantum 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
#... |
import gym
import numpy as np
import random
import scipy.special
import rl
from gym.spaces import Box
from gym.spaces import Discrete
from rl.np.distributions import CategoricalProbabilityDistributionType
from rl.np.distributions import DiagonalGaussianProbabilityDistributionType
class ProbabilityDistributionTypeFact... |
<gh_stars>0
import numpy as np
import scipy
from scipy.stats import norm
#import astropy functions for use in bootstrap (optional)
try:
import astropy
from astropy.stats import biweight_location
except ImportError:
astropy = None
# BCES fitting
# ===============
def bces(y1, y1err, y2, ... |
## EKE SPEC PLOT
from __future__ import print_function
path1 = '/network/aopp/cirrus/pred/kloewer/swm_back_ronew/'
path2 = '/network/aopp/cirrus/pred/kloewer/swm_bf_cntrl/data/'
outpath = '/network/home/aopp/kloewer/swm/paperplot/'
import os; os.chdir(path2) # change working directory
import numpy as np
from scipy im... |
import numpy as np
from skimage.exposure import equalize_adapthist
import torch
from scipy.ndimage import gaussian_filter
import scipy
import random
import torch as th
from PIL import Image
from scipy.interpolate import RectBivariateSpline
class MyRandomImageContrastTransform(object):
def __init__(self, random_s... |
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