repo stringlengths 2 99 | file stringlengths 14 239 | code stringlengths 20 3.99M | file_length int64 20 3.99M | avg_line_length float64 9.73 128 | max_line_length int64 11 86.4k | extension_type stringclasses 1
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trieste-develop | trieste-develop/trieste/models/interfaces.py |
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any, Callable, Generic, Optional, TypeVar
import gpflow
import tensorflow as tf
from typing_extensions import Protocol, runtime_checkable
from ..data import Dataset
from ..types import TensorType
from ..utils import DEFAULTS
f... | 30,175 | 41.263305 | 100 | py |
trieste-develop | trieste-develop/trieste/models/optimizer.py |
r"""
This module contains common optimizers based on :class:`~tf.optimizers.Optimizer` that can be used
with models. Specific models can also sub-class these optimizers or implement their own, and should
register their loss functions using a :func:`create_loss_function`.
"""
from __future__ import annotations
import... | 9,230 | 37.949367 | 100 | py |
trieste-develop | trieste-develop/trieste/models/gpflux/sampler.py |
from __future__ import annotations
from abc import ABC
from typing import Callable, cast
import gpflow.kernels
import tensorflow as tf
from gpflow.inducing_variables import InducingPoints
from gpflux.layers import GPLayer, LatentVariableLayer
from gpflux.layers.basis_functions.fourier_features import RandomFourierFe... | 20,609 | 40.302605 | 100 | py |
trieste-develop | trieste-develop/trieste/models/gpflux/builders.py |
"""
This file contains builders for GPflux models supported in Trieste. We found the default
configurations used here to work well in most situation, but they should not be taken as
universally good solutions.
"""
from __future__ import annotations
from typing import Optional
import gpflow
import numpy as np
import... | 6,191 | 38.43949 | 100 | py |
trieste-develop | trieste-develop/trieste/models/gpflux/models.py |
from __future__ import annotations
from typing import Any, Callable, Optional
import dill
import gpflow
import tensorflow as tf
from gpflow.inducing_variables import InducingPoints
from gpflux.layers import GPLayer, LatentVariableLayer
from gpflux.models import DeepGP
from tensorflow.python.keras.callbacks import Ca... | 18,255 | 44.526185 | 100 | py |
trieste-develop | trieste-develop/trieste/models/gpflux/interface.py |
from __future__ import annotations
from abc import ABC, abstractmethod
import tensorflow as tf
from gpflow.base import Module
from ...types import TensorType
from ..interfaces import SupportsGetObservationNoise
from ..optimizer import KerasOptimizer
class GPfluxPredictor(SupportsGetObservationNoise, ABC):
"""... | 3,501 | 37.483516 | 98 | py |
trieste-develop | trieste-develop/trieste/models/keras/sampler.py |
"""
This module is the home of the sampling functionality required by some
of the Trieste's Keras model wrappers.
"""
from __future__ import annotations
from typing import Dict, Optional
import tensorflow as tf
from ...types import TensorType
from ...utils import DEFAULTS, flatten_leading_dims
from ..interfaces im... | 9,679 | 41.643172 | 100 | py |
trieste-develop | trieste-develop/trieste/models/keras/utils.py |
from __future__ import annotations
from typing import Optional
import tensorflow as tf
import tensorflow_probability as tfp
from ...data import Dataset
from ...types import TensorType
def get_tensor_spec_from_data(dataset: Dataset) -> tuple[tf.TensorSpec, tf.TensorSpec]:
r"""
Extract tensor specifications... | 5,241 | 37.262774 | 100 | py |
trieste-develop | trieste-develop/trieste/models/keras/architectures.py |
"""
This file contains implementations of neural network architectures with Keras.
"""
from __future__ import annotations
from abc import abstractmethod
from typing import Any, Callable, Sequence
import dill
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow_probability.... | 14,523 | 40.497143 | 100 | py |
trieste-develop | trieste-develop/trieste/models/keras/builders.py |
"""
This file contains builders for Keras models supported in Trieste. We found the default
configurations used here to work well in most situation, but they should not be taken as
universally good solutions.
"""
from __future__ import annotations
from typing import Union
import tensorflow as tf
from ...data impor... | 3,471 | 40.831325 | 99 | py |
trieste-develop | trieste-develop/trieste/models/keras/models.py |
from __future__ import annotations
import re
from typing import Any, Dict, Optional
import dill
import tensorflow as tf
import tensorflow_probability as tfp
import tensorflow_probability.python.distributions as tfd
from tensorflow.python.keras.callbacks import Callback
from ... import logging
from ...data import Da... | 25,439 | 47.923077 | 100 | py |
trieste-develop | trieste-develop/trieste/models/keras/interface.py |
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Optional
import tensorflow as tf
import tensorflow_probability as tfp
from typing_extensions import Protocol, runtime_checkable
from ...types import TensorType
from ..interfaces import ProbabilisticModel
from ..optimizer impor... | 4,335 | 36.37931 | 100 | py |
trieste-develop | trieste-develop/trieste/models/gpflow/inducing_point_selectors.py | """
This module is the home of Trieste's functionality for choosing the inducing points
of sparse variational Gaussian processes (i.e. our :class:`SparseVariational` wrapper).
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Generic
import gpflow
import tensorflow as tf
... | 18,538 | 39.655702 | 100 | py |
trieste-develop | trieste-develop/trieste/models/gpflow/sampler.py | """
This module is the home of the sampling functionality required by Trieste's
GPflow wrappers.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Callable, Optional, Tuple, TypeVar, Union, cast
import tensorflow as tf
import tensorflow_probability as tfp
from gpflow.kerne... | 40,450 | 42.402361 | 100 | py |
trieste-develop | trieste-develop/trieste/models/gpflow/utils.py |
from __future__ import annotations
from typing import Tuple, Union
import gpflow
import tensorflow as tf
import tensorflow_probability as tfp
from ...data import Dataset
from ...types import TensorType
from ...utils import DEFAULTS
from ..optimizer import BatchOptimizer, Optimizer
from .interface import GPflowPredi... | 14,693 | 41.964912 | 99 | py |
trieste-develop | trieste-develop/trieste/models/gpflow/builders.py |
"""
This module contains builders for GPflow models supported in Trieste. We found the default
configurations used here to work well in most situation, but they should not be taken as
universally good solutions.
"""
from __future__ import annotations
import math
from typing import Optional, Sequence, Type
import gp... | 27,128 | 42.68599 | 99 | py |
trieste-develop | trieste-develop/trieste/models/gpflow/models.py |
from __future__ import annotations
from typing import Optional, Sequence, Tuple, Union, cast
import gpflow
import tensorflow as tf
import tensorflow_probability as tfp
from gpflow.conditionals.util import sample_mvn
from gpflow.inducing_variables import (
SeparateIndependentInducingVariables,
SharedIndepende... | 90,637 | 43.825915 | 100 | py |
trieste-develop | trieste-develop/trieste/models/gpflow/interface.py |
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any, Optional
import gpflow
import tensorflow as tf
from gpflow.models import GPModel
from gpflow.posteriors import BasePosterior, PrecomputeCacheType
from typing_extensions import Protocol
from ... import logging
from ...data... | 7,905 | 37.754902 | 100 | py |
trieste-develop | trieste-develop/trieste/models/gpflow/optimizer.py |
r"""
This module registers the GPflow specific loss functions.
"""
from __future__ import annotations
from typing import Any, Callable, Optional
import tensorflow as tf
from gpflow.models import ExternalDataTrainingLossMixin, InternalDataTrainingLossMixin
from tensorflow.python.data.ops.iterator_ops import OwnedIte... | 3,374 | 34.526316 | 91 | py |
trieste-develop | trieste-develop/trieste/utils/misc.py | from __future__ import annotations
from abc import ABC, abstractmethod
from time import perf_counter
from types import TracebackType
from typing import Any, Callable, Generic, Mapping, NoReturn, Optional, Tuple, Type, TypeVar
import numpy as np
import tensorflow as tf
from tensorflow.python.util import nest
from typi... | 11,630 | 28.371212 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/sampler.py | """
This module is the home of the sampling functionality required by Trieste's
acquisition functions.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Callable, Generic
import tensorflow as tf
import tensorflow_probability as tfp
from scipy.optimize import bisect
from .... | 11,521 | 41.674074 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/utils.py | import functools
from typing import Tuple, Union
import tensorflow as tf
from ..data import Dataset
from ..space import SearchSpaceType
from ..types import TensorType
from .interface import AcquisitionFunction
from .optimizer import AcquisitionOptimizer
def split_acquisition_function(
fn: AcquisitionFunction,
... | 5,297 | 37.391304 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/interface.py | """
This module contains the interfaces relating to acquisition function --- functions that estimate
the utility of evaluating sets of candidate points.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Callable, Generic, Mapping, Optional
from ..data import Dataset
from ..... | 16,951 | 41.916456 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/combination.py | from __future__ import annotations
from abc import abstractmethod
from collections.abc import Mapping, Sequence
from typing import Callable, Optional
import tensorflow as tf
from ..data import Dataset
from ..models import ProbabilisticModelType
from ..types import Tag, TensorType
from .interface import AcquisitionFu... | 6,417 | 36.752941 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/optimizer.py |
r"""
This module contains functionality for optimizing
:data:`~trieste.acquisition.AcquisitionFunction`\ s over :class:`~trieste.space.SearchSpace`\ s.
"""
from __future__ import annotations
from typing import Any, Callable, List, Optional, Sequence, Tuple, Union, cast
import greenlet as gr
import numpy as np
impor... | 30,061 | 42.254676 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/function/greedy_batch.py | """
This module contains local penalization-based acquisition function builders.
"""
from __future__ import annotations
from typing import Callable, Dict, Mapping, Optional, Union, cast
import gpflow
import tensorflow as tf
import tensorflow_probability as tfp
from typing_extensions import Protocol, runtime_checkable... | 34,562 | 42.257822 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/function/utils.py | """
This module contains utility functions for acquisition functions.
"""
from typing import Callable, Tuple
import tensorflow as tf
from tensorflow_probability import distributions as tfd
from ...types import TensorType
# Multivariate Normal CDF
class MultivariateNormalCDF:
def __init__(
self,
... | 7,456 | 36.285 | 87 | py |
trieste-develop | trieste-develop/trieste/acquisition/function/multi_objective.py | """
This module contains multi-objective acquisition function builders.
"""
from __future__ import annotations
import math
from itertools import combinations, product
from typing import Callable, Mapping, Optional, Sequence, cast
import tensorflow as tf
import tensorflow_probability as tfp
from ...data import Datase... | 33,929 | 43.821664 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/function/continuous_thompson_sampling.py | """
This module contains acquisition function builders for continuous Thompson sampling.
"""
from __future__ import annotations
from typing import Any, Callable, Optional, Type
import tensorflow as tf
from ...data import Dataset
from ...models.interfaces import HasTrajectorySampler, TrajectoryFunction, TrajectoryFun... | 10,279 | 40.788618 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/function/function.py | """
This module contains acquisition function builders, which build and define our acquisition
functions --- functions that estimate the utility of evaluating sets of candidate points.
"""
from __future__ import annotations
from typing import Callable, Mapping, Optional, cast
import tensorflow as tf
import tensorflow... | 77,739 | 38.262626 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/function/active_learning.py |
"""
This module contains acquisition function builders and acquisition functions for Bayesian active
learning.
"""
from __future__ import annotations
import math
from typing import Optional, Sequence, Union
import tensorflow as tf
import tensorflow_probability as tfp
from ...data import Dataset
from ...models impo... | 20,764 | 39.320388 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/function/entropy.py | """
This module contains entropy-based acquisition function builders.
"""
from __future__ import annotations
from typing import List, Optional, TypeVar, cast, overload
import tensorflow as tf
import tensorflow_probability as tfp
from typing_extensions import Protocol, runtime_checkable
from ...data import Dataset, a... | 36,326 | 41.787986 | 100 | py |
trieste-develop | trieste-develop/trieste/acquisition/multi_objective/pareto.py | """ This module contains functions and classes for Pareto based multi-objective optimization. """
from __future__ import annotations
try:
import cvxpy as cp
except ImportError: # pragma: no cover (tested but not by coverage)
cp = None
import numpy as np
import tensorflow as tf
from ...types import TensorType... | 11,521 | 39.006944 | 99 | py |
trieste-develop | trieste-develop/trieste/acquisition/multi_objective/dominance.py | """This module contains functionality for computing the non-dominated set
given a set of data points."""
from __future__ import annotations
import tensorflow as tf
from ...types import TensorType
def non_dominated(observations: TensorType) -> tuple[TensorType, TensorType]:
"""
Computes the non-dominated set... | 2,789 | 38.295775 | 97 | py |
trieste-develop | trieste-develop/trieste/acquisition/multi_objective/partition.py | """This module contains functions of different methods for
partitioning the dominated/non-dominated region in multi-objective optimization problems."""
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Optional
import tensorflow as tf
from ...types import TensorType
from ..... | 16,741 | 41.492386 | 100 | py |
trieste-develop | trieste-develop/trieste/experimental/plotting/plotting_plotly.py |
from __future__ import annotations
from typing import Callable, Optional
import numpy as np
import plotly.graph_objects as go
import tensorflow as tf
from plotly.subplots import make_subplots
from trieste.models.interfaces import ProbabilisticModel
from trieste.types import TensorType
from trieste.utils import to_n... | 8,489 | 31.653846 | 99 | py |
trieste-develop | trieste-develop/trieste/experimental/plotting/inequality_constraints.py |
from __future__ import annotations
from abc import abstractmethod
from typing import Optional, Type, cast
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.figure import Figure
from typing_extensions import Protocol
from ...space import SearchSpace
from ...types import TensorType
from .plotting imp... | 6,314 | 32.590426 | 88 | py |
trieste-develop | trieste-develop/trieste/experimental/plotting/plotting.py |
from __future__ import annotations
from typing import Callable, Optional, Sequence
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from gpflow.models import GPModel
from matplotlib import cm
from matplotlib.axes import Axes
from matplotlib.collections import Collection
from matplotlib.cont... | 17,758 | 32.070764 | 99 | py |
trieste-develop | trieste-develop/docs/notebooks/quickrun/quickrun.py |
"""
A script to apply modifications to the notebook scripts based on YAML config,
used to make them run more quickly in continuous integration.
"""
from jsonschema import validate
from pathlib import Path
import re
import sys
import yaml
import logging
import argparse
logging.basicConfig(format="%(asctime)s %(levelna... | 4,104 | 30.821705 | 100 | py |
ba-complement | ba-complement-master/experimental/experimental-compare.py |
"""
Script for automated experimental evaluation.
@title experimental.py
@author Vojtech Havlena, June 2019
"""
import sys
import getopt
import subprocess
import string
import re
import os
import os.path
import resource
import xml.etree.ElementTree as ET
VALIDLINE = -2
TIMELINE = -1
STATESLINE = -3
DELAYSIM = -4
... | 2,684 | 23.189189 | 101 | py |
ba-complement | ba-complement-master/experimental/experimental.py |
"""
Script for automated experimental evaluation.
@title experimental.py
@author Vojtech Havlena, April 2019
"""
import sys
import getopt
import subprocess
import string
import re
import os
import os.path
import resource
VALIDLINE = -2
TIMELINE = -1
STATESLINE = -2
DELAYSIM = -4
TIMEOUT = 300 #in seconds
QUOTIENT... | 3,042 | 25.008547 | 108 | py |
tensiometer | tensiometer-master/.material.py | """
This is random material, do not read it :)
"""
def _vec_to_log_pdm(vec, d):
"""
"""
# get indexes:
ind = np.tril_indices(d, 0)
# initialize:
mat = np.zeros((d, d))
mat[ind] = vec
# take exponential of the diagonal to ensure positivity:
mat[np.diag_indices(d)] = np.exp(np.diagon... | 3,098 | 28.514286 | 127 | py |
tensiometer | tensiometer-master/setup.py | import re
import os
import sys
import setuptools
# warn against python 2
if sys.version_info[0] == 2:
print('tensiometer does not support Python 2, \
please upgrade to Python 3')
sys.exit(1)
# version control:
def find_version():
version_file = open(os.path.join(os.path.dirname(__file__),
... | 2,749 | 33.375 | 103 | py |
tensiometer | tensiometer-master/tensiometer/gaussian_tension.py | """
This file contains the functions and utilities to compute agreement and
disagreement between two different chains using a Gaussian approximation
for the posterior.
For more details on the method implemented see
`arxiv 1806.04649 <https://arxiv.org/pdf/1806.04649.pdf>`_
and `arxiv 1912.04880 <https://arxiv.org/pdf/... | 51,236 | 43.246114 | 111 | py |
tensiometer | tensiometer-master/tensiometer/cosmosis_interface.py | """
File with tools to interface Cosmosis chains with GetDist.
"""
"""
For testing purposes:
chain = loadMCSamples('./../test_chains/1p2_SN1_zcut0p3_abs')
chain_root = './test_chains/DES_multinest_cosmosis'
chain_root = './chains_lcdm/chain_1x2pt_lcdm'
chain_min_root = './chains_lcdm/chain_1x2pt_lcdm_MAP.maxlike'
pa... | 15,767 | 37.179177 | 97 | py |
tensiometer | tensiometer-master/tensiometer/chains_convergence.py | """
This file contains some functions to study convergence of the chains and
to compare the two posteriors.
"""
"""
For test purposes:
from getdist import loadMCSamples, MCSamples, WeightedSamples
chain = loadMCSamples('./test_chains/DES')
chains = chain
param_names = None
import tensiometer.utilities as utils
import... | 14,904 | 36.638889 | 104 | py |
tensiometer | tensiometer-master/tensiometer/utilities.py | """
This file contains some utilities that are used in the tensiometer package.
"""
# initial imports:
import numpy as np
import scipy
import scipy.special
from scipy.linalg import sqrtm
from getdist import MCSamples
def from_confidence_to_sigma(P):
"""
Transforms a probability to effective number of sigma... | 15,550 | 34.997685 | 88 | py |
tensiometer | tensiometer-master/tensiometer/experimental.py | """
Experimental features.
For test purposes:
import os, sys
import time
import gc
from numba import jit
import numpy as np
import getdist.chains as gchains
gchains.print_load_details = False
from getdist import MCSamples, WeightedSamples
import scipy
from scipy.linalg import sqrtm
from scipy.integrate import simps
f... | 4,211 | 26.350649 | 121 | py |
tensiometer | tensiometer-master/tensiometer/mcmc_tension/kde.py | """
"""
"""
For test purposes:
from getdist import loadMCSamples, MCSamples, WeightedSamples
chain_1 = loadMCSamples('./test_chains/DES')
chain_2 = loadMCSamples('./test_chains/Planck18TTTEEE')
chain_12 = loadMCSamples('./test_chains/Planck18TTTEEE_DES')
chain_prior = loadMCSamples('./test_chains/prior')
import ten... | 43,456 | 41.688605 | 151 | py |
Atari-5 | Atari-5-main/atari_util.py | import matplotlib.pyplot as plt
cmap10 = plt.get_cmap('tab10')
cmap20 = plt.get_cmap('tab20')
def color_fade(x, factor=0.5):
if len(x) == 3:
r,g,b = x
a = 1.0
else:
r,g,b,a = x
r = (1*factor+(1-factor)*r)
g = (1*factor+(1-factor)*g)
b = (1*factor+(1-factor)*b)
return (r... | 5,851 | 23.082305 | 112 | py |
Atari-5 | Atari-5-main/atari5.py | import numpy as np
import pandas
import pandas as pd
import itertools
import sklearn
import sklearn.linear_model
import statsmodels
import statsmodels.api as sm
import json
import csv
import matplotlib.pyplot as plt
import multiprocessing
import functools
import time
from sklearn.model_selection import cross_val_score
... | 25,279 | 33.301221 | 157 | py |
white_box_rarl | white_box_rarl-main/wbrarl_plotting.py |
from pathlib import Path
import numpy as np
import pickle
import matplotlib.pyplot as plt
from matplotlib import rc
from scipy import stats
rc('font', **{'family': 'serif', 'serif': ['Palatino']})
plt.rcParams['pdf.fonttype'] = 42
results_path = Path('./results/')
N_TRAIN_STEPS = 2000000
FS = 15
N_EXCLUDE = 20
TOTAL... | 12,269 | 34.877193 | 121 | py |
white_box_rarl | white_box_rarl-main/wbrarl.py | import sys
import os
import time
import random
import argparse
import multiprocessing
import pickle
import copy
from multiprocessing import freeze_support
import numpy as np
import torch
import gym
from stable_baselines3.ppo import PPO
from stable_baselines3.sac import SAC
from stable_baselines3.common.vec_env import S... | 24,786 | 43.341682 | 148 | py |
neurotron_experiments | neurotron_experiments-main/run_sim05.py | # %% Import packages
import numpy as np
from pathlib import Path
from neurotron import NeuroTron
from sim_setup import output_path, sim05_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Set the seed
np.random.seed(sim05_setup['seed'])
# %% Instantiate Neur... | 956 | 22.341463 | 106 | py |
neurotron_experiments | neurotron_experiments-main/plot_sim01.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim01_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,838 | 24.123894 | 120 | py |
neurotron_experiments | neurotron_experiments-main/run_sim07.py | # %% Import packages
import numpy as np
from pathlib import Path
from neurotron import NeuroTron
from sim_setup import output_path, sim07_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Set the seed
np.random.seed(sim07_setup['seed'])
# %% Instantiate Neur... | 956 | 22.341463 | 106 | py |
neurotron_experiments | neurotron_experiments-main/plot_sim05.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim05_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,830 | 24.053097 | 119 | py |
neurotron_experiments | neurotron_experiments-main/plot_sim06.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim06_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,830 | 24.053097 | 119 | py |
neurotron_experiments | neurotron_experiments-main/plot_tron_theta_no_attack.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim01_setup, sim02_setup, sim03_setup, sim04_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_e... | 3,020 | 22.787402 | 108 | py |
neurotron_experiments | neurotron_experiments-main/plot_sim04.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim04_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,841 | 24.150442 | 120 | py |
neurotron_experiments | neurotron_experiments-main/run_sim02.py | # %% Import packages
import numpy as np
from pathlib import Path
from neurotron import NeuroTron
from sim_setup import output_path, sim02_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Set the seed
np.random.seed(sim02_setup['seed'])
# %% Instantiate Neur... | 956 | 22.341463 | 106 | py |
neurotron_experiments | neurotron_experiments-main/plot_tron_merged_theta.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim01_setup, sim02_setup, sim03_setup, sim04_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_e... | 4,623 | 28.832258 | 108 | py |
neurotron_experiments | neurotron_experiments-main/neurotron_torch.py | # %% [markdown]
# # Settings
# %%
import torch
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from torch.utils.data import DataLoader... | 8,128 | 25.478827 | 122 | py |
neurotron_experiments | neurotron_experiments-main/plot_tron_q_assist_sim.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_neuron1_error_loaded = []
for k in range(3):
tro... | 2,981 | 20.608696 | 126 | py |
neurotron_experiments | neurotron_experiments-main/plot_merged_sim05.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim05_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,834 | 23.025424 | 104 | py |
neurotron_experiments | neurotron_experiments-main/sim_setup.py | # %% Import packages
import numpy as np
from pathlib import Path
# %% Set output path
output_path = Path().joinpath('output')
# %% Setup for simulation 1: data ~ normal(mu=0, sigma=1), varying theta_{*}
sim01_setup = {
'sample_data' : lambda s : np.random.normal(loc=0.0, scale=1.0, size=s),
'filterlist' :... | 5,716 | 38.157534 | 84 | py |
neurotron_experiments | neurotron_experiments-main/plot_merged_sim01.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim01_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,851 | 23.169492 | 112 | py |
neurotron_experiments | neurotron_experiments-main/plot_sim08.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim08_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,833 | 24.079646 | 119 | py |
neurotron_experiments | neurotron_experiments-main/plot_merged_sim03.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim03_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,851 | 23.169492 | 112 | py |
neurotron_experiments | neurotron_experiments-main/plot_sim07.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim07_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,830 | 24.053097 | 119 | py |
neurotron_experiments | neurotron_experiments-main/plot_merged_sim08.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim08_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,834 | 23.025424 | 104 | py |
neurotron_experiments | neurotron_experiments-main/run_sim03.py | # %% Import packages
import numpy as np
from pathlib import Path
from neurotron import NeuroTron
from sim_setup import output_path, sim03_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Set the seed
np.random.seed(sim03_setup['seed'])
# %% Instantiate Neur... | 956 | 22.341463 | 106 | py |
neurotron_experiments | neurotron_experiments-main/plot_merged_sim06.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim06_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,834 | 23.025424 | 104 | py |
neurotron_experiments | neurotron_experiments-main/run_sim08.py | # %% Import packages
import numpy as np
from pathlib import Path
from neurotron import NeuroTron
from sim_setup import output_path, sim08_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Set the seed
np.random.seed(sim08_setup['seed'])
# %% Instantiate Neur... | 956 | 22.341463 | 106 | py |
neurotron_experiments | neurotron_experiments-main/run_sim01.py | # %% Import packages
import numpy as np
from pathlib import Path
from neurotron import NeuroTron
from sim_setup import output_path, sim01_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Set the seed
np.random.seed(sim01_setup['seed'])
# %% Instantiate Neur... | 956 | 22.341463 | 106 | py |
neurotron_experiments | neurotron_experiments-main/plot_merged_sim07.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim07_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,834 | 23.025424 | 104 | py |
neurotron_experiments | neurotron_experiments-main/plot_merged_sim04.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim04_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,851 | 23.169492 | 112 | py |
neurotron_experiments | neurotron_experiments-main/run_sim04.py | # %% Import packages
import numpy as np
from pathlib import Path
from neurotron import NeuroTron
from sim_setup import output_path, sim04_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Set the seed
np.random.seed(sim04_setup['seed'])
# %% Instantiate Neur... | 956 | 22.341463 | 106 | py |
neurotron_experiments | neurotron_experiments-main/plot_tron_merged_beta.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim05_setup, sim06_setup, sim07_setup, sim08_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_e... | 4,409 | 27.451613 | 108 | py |
neurotron_experiments | neurotron_experiments-main/run_sim06.py | # %% Import packages
import numpy as np
from pathlib import Path
from neurotron import NeuroTron
from sim_setup import output_path, sim06_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Set the seed
np.random.seed(sim06_setup['seed'])
# %% Instantiate Neur... | 956 | 22.341463 | 106 | py |
neurotron_experiments | neurotron_experiments-main/neurotron.py | import numpy as np
class NeuroTron:
def __init__(self, sample_data=None, w_star=None, d=None, eta_tron=None, eta_sgd=None, b=None, width=None, filter=None):
self.sample_data = sample_data
self.reset(w_star, d, eta_tron, b, width, filter)
def reset(self, w_star, d, eta_tron, b, width, filter, ... | 5,583 | 33.68323 | 124 | py |
neurotron_experiments | neurotron_experiments-main/plot_merged_sim02.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim02_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,851 | 23.169492 | 112 | py |
neurotron_experiments | neurotron_experiments-main/plot_sim03.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim03_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,838 | 24.123894 | 120 | py |
neurotron_experiments | neurotron_experiments-main/plot_sim02.py | # %% Import packages
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from sim_setup import output_path, sim02_setup
# %% Create output path if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# %% Load numerical output
tron_error_loaded = np.loadtxt(output_path.jo... | 2,838 | 24.123894 | 120 | py |
presto | presto-master/setup.py | from __future__ import print_function
import os
import sys
import numpy
# setuptools has to be imported before numpy.distutils.core
import setuptools
from numpy.distutils.core import Extension, setup
version = "4.0"
define_macros = []
undef_macros = []
extra_compile_args = ["-DUSEFFTW"]
include_dirs = [numpy.get_inc... | 3,895 | 40.010526 | 96 | py |
presto | presto-master/python/binresponses/monte_short.py | from __future__ import print_function
from builtins import range
from time import clock
from math import *
from Numeric import *
from presto import *
from miscutils import *
from Statistics import *
import Pgplot
# Some admin variables
showplots = 0 # True or false
showsumplots = 0 # True or false
debugou... | 3,677 | 36.530612 | 79 | py |
presto | presto-master/python/binresponses/monte_ffdot.py | from __future__ import print_function
from builtins import range
from time import clock
from math import *
from Numeric import *
from presto import *
from miscutils import *
from Statistics import *
# Some admin variables
parallel = 0 # True or false
showplots = 0 # True or false
debugout = 0 ... | 7,526 | 40.585635 | 85 | py |
presto | presto-master/python/binresponses/monte_sideb.py | from __future__ import print_function
from builtins import range
from time import clock
from math import *
from Numeric import *
from presto import *
from miscutils import *
from Statistics import *
from random import expovariate
import RNG
global theo_sum_pow, b_pows, bsum_pows, newpows, noise, fftlen
# Some admin v... | 9,594 | 37.075397 | 97 | py |
presto | presto-master/python/binresponses/montebinresp.py | from __future__ import print_function
from builtins import range
from time import clock
from math import *
from Numeric import *
from presto import *
from miscutils import *
from Statistics import *
# Some admin variables
parallel = 0 # True or false
showplots = 1 # True or false
debugout = 1 ... | 12,905 | 46.623616 | 85 | py |
presto | presto-master/python/presto/sifting.py | from __future__ import print_function
from __future__ import absolute_import
from builtins import zip, str, range, object
from operator import attrgetter
import sys, re, os, copy
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import os.path
import glob
from presto import infodata
from presto.prest... | 54,077 | 39.146993 | 99 | py |
presto | presto-master/python/presto/infodata.py | from builtins import object
## Automatically adapted for numpy Apr 14, 2006 by convertcode.py
class infodata(object):
def __init__(self, filenm):
self.breaks = 0
for line in open(filenm, encoding="latin-1"):
if line.startswith(" Data file name"):
self.basenm = line.split... | 6,965 | 47.041379 | 98 | py |
presto | presto-master/python/presto/binary_psr.py | from __future__ import print_function
from __future__ import absolute_import
from builtins import object
import numpy as Num
from presto import parfile, psr_utils
from presto.psr_constants import *
def myasarray(a):
if type(a) in [type(1.0),type(1),type(1),type(1j)]:
a = Num.asarray([a])
if len(a) == 0... | 10,195 | 38.366795 | 84 | py |
presto | presto-master/python/presto/parfile.py | from __future__ import print_function
from __future__ import absolute_import
from builtins import object
import six
import math, re
from presto import psr_utils as pu
from presto import psr_constants as pc
try:
from slalib import sla_ecleq, sla_eqecl, sla_eqgal
slalib = True
except ImportError:
slalib = Fal... | 10,504 | 41.703252 | 96 | py |
presto | presto-master/python/presto/events.py | from __future__ import print_function
import bisect
from presto.psr_constants import PI, TWOPI, PIBYTWO
from presto.simple_roots import newton_raphson
from scipy.special import iv, chdtri, ndtr, ndtri
from presto.cosine_rand import *
import numpy as np
def sine_events(pulsed_frac, Nevents, phase=0.0):
"""
sin... | 18,498 | 40.947846 | 89 | py |
presto | presto-master/python/presto/mpfit.py | """
Perform Levenberg-Marquardt least-squares minimization, based on MINPACK-1.
AUTHORS
The original version of this software, called LMFIT, was written in FORTRAN
as part of the MINPACK-1 package by XXX.
Craig Markwardt converted the FORTRAN code to IDL. The information for ... | 88,531 | 38.190792 | 97 | py |
presto | presto-master/python/presto/sigproc.py | from __future__ import print_function
from __future__ import absolute_import
from builtins import zip
import os
import struct
import sys
import math
import warnings
from presto.psr_constants import ARCSECTORAD
telescope_ids = {"Fake": 0, "Arecibo": 1, "ARECIBO 305m": 1,
"Ooty": 2, "Nancay": 3, "Parke... | 7,132 | 31.130631 | 92 | py |
presto | presto-master/python/presto/waterfaller.py | ../../bin/waterfaller.py | 24 | 24 | 24 | py |
presto | presto-master/python/presto/spectra.py | from builtins import str
from builtins import range
from builtins import object
import copy
import numpy as np
import scipy.signal
from presto import psr_utils
class Spectra(object):
"""A class to store spectra. This is mainly to provide
reusable functionality.
"""
def __init__(self, freqs, dt, da... | 12,864 | 36.616959 | 88 | py |
presto | presto-master/python/presto/psr_utils.py | from __future__ import print_function
from __future__ import absolute_import
from builtins import str
from builtins import range
import bisect
import numpy as Num
import numpy.fft as FFT
from scipy.special import ndtr, ndtri, chdtrc, chdtri, fdtrc, i0, kolmogorov
from scipy.optimize import leastsq
import scipy.optimize... | 75,060 | 36.399601 | 112 | py |
presto | presto-master/python/presto/psr_constants.py | ## Automatically adapted for numpy Apr 14, 2006 by convertcode.py
ARCSECTORAD = float('4.8481368110953599358991410235794797595635330237270e-6')
RADTOARCSEC = float('206264.80624709635515647335733077861319665970087963')
SECTORAD = float('7.2722052166430399038487115353692196393452995355905e-5')
RADTOSEC = float('1... | 1,369 | 51.692308 | 77 | py |
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