repo_name stringlengths 6 96 | path stringlengths 4 180 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 906 722k | license stringclasses 15
values |
|---|---|---|---|---|---|
kmike/scikit-learn | sklearn/utils/__init__.py | 3 | 10094 | """
The :mod:`sklearn.utils` module includes various utilites.
"""
from collections import Sequence
import numpy as np
from scipy.sparse import issparse
import warnings
from .murmurhash import murmurhash3_32
from .validation import (as_float_array, check_arrays, safe_asarray,
assert_all_fini... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.20/_downloads/76822bb92a8465181ec2a7ee96ca8cf4/plot_decoding_csp_timefreq.py | 1 | 6457 | """
============================================================================
Decoding in time-frequency space data using the Common Spatial Pattern (CSP)
============================================================================
The time-frequency decomposition is estimated by iterating over raw data that
has be... | bsd-3-clause |
bijanfallah/OI_CCLM | src/RMSE_MAPS_INGO.py | 1 | 2007 | # Program to show the maps of RMSE averaged over time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
import os
from netCDF4 import Dataset as NetCDFFile
import numpy as np
from CCLM_OUTS import Plot_CCLM
# option == 1 -> shift 4 with default cclm domain and nboundlines = 3
# option == 2... | mit |
lancezlin/ml_template_py | lib/python2.7/site-packages/sklearn/metrics/tests/test_score_objects.py | 15 | 17443 | import pickle
import tempfile
import shutil
import os
import numbers
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regexp
from sklearn.utils.t... | mit |
ChanChiChoi/scikit-learn | examples/model_selection/plot_roc.py | 146 | 3697 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X a... | bsd-3-clause |
belltailjp/scikit-learn | sklearn/decomposition/base.py | 313 | 5647 | """Principal Component Analysis Base Classes"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <d.engemann@fz-juelich.de>
# Kyle Kastner <kastnerkyle@gmail.com>
#
# Licen... | bsd-3-clause |
tosolveit/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
jblackburne/scikit-learn | sklearn/neural_network/rbm.py | 46 | 12291 | """Restricted Boltzmann Machine
"""
# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
# Vlad Niculae
# Gabriel Synnaeve
# Lars Buitinck
# License: BSD 3 clause
import time
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator
from ..base import TransformerMixi... | bsd-3-clause |
jorik041/scikit-learn | sklearn/linear_model/randomized_l1.py | 95 | 23365 | """
Randomized Lasso/Logistic: feature selection based on Lasso and
sparse Logistic Regression
"""
# Author: Gael Varoquaux, Alexandre Gramfort
#
# License: BSD 3 clause
import itertools
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy.sparse import issparse
from scipy import spar... | bsd-3-clause |
jingxiang-li/kaggle-yelp | model/level3_model_rf.py | 1 | 5669 | from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import f1_score
import... | mit |
JeanKossaifi/scikit-learn | sklearn/tree/tests/test_tree.py | 48 | 47506 | """
Testing for the tree module (sklearn.tree).
"""
import pickle
from functools import partial
from itertools import product
import platform
import numpy as np
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
from sklearn.random_projection import sparse_rand... | bsd-3-clause |
kaczla/PJN | src/Przecinki/scikit.py | 1 | 1048 | #!/usr/bin/python2
# -*- coding: utf-8 -*-
import sys
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.cross_validation import cross_val_predict
from sklearn import linear_model
from sklearn import datasets
X = []
Y = []
for line in sys.stdin:
line = line.rstrip()
X... | gpl-2.0 |
chaluemwut/fbserver | venv/lib/python2.7/site-packages/sklearn/neighbors/base.py | 1 | 24541 | """Base and mixin classes for nearest neighbors"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output... | apache-2.0 |
Vimos/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
siutanwong/scikit-learn | examples/text/document_clustering.py | 230 | 8356 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
COL-IU/XLSearch | xlsearch_train.py | 1 | 5042 | import sys
import pickle
import os
import getopt
from time import ctime
import numpy as np
usage = '''
USAGE: python xlsearch_train.py -l [path to xlsearch library]
-p [parameter file]
-o [output file]'''
(pairs, args) = getopt.getopt(sys.argv[1:], 'l:p:... | mit |
meduz/scikit-learn | examples/linear_model/plot_lasso_lars.py | 363 | 1080 | #!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================
Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regulariza... | bsd-3-clause |
End of preview. Expand in Data Studio
YAML Metadata Warning:The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
dataset_info: features:
- name: repo_name dtype: string
- name: path dtype: string
- name: copies dtype: string
- name: size dtype: string
- name: content dtype: string
- name: license dtype: string splits:
- name: train num_bytes: 3147402833.3951 num_examples: 241075
- name: valid num_bytes: 17472318.29500301 num_examples: 1312 download_size: 966099631 dataset_size: 3164875151.690103 configs:
- config_name: default
data_files:
- split: train path: data/train-*
- split: valid path: data/valid-* license: mit
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