Hands-On Machine Learning with Scikit-Learn & TensorFlow 中文版+英文版电子书+源码+数据
Hands-On Machine Learning with Scikit-Learn & TensorFlow 中文版+英文版电子书+源码+数据
代码片段和文件信息
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 195 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据.gitignore
文件 284756 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 1_the_machine_learning_landscape.ipynb
文件 1350273 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 2_end_to_end_machine_learning_project.ipynb
文件 447511 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 3_classification.ipynb
文件 851389 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 4_training_linear_models.ipynb
文件 914359 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 5_support_vector_machines.ipynb
文件 204214 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 6_decision_trees.ipynb
文件 553305 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 7_ensemble_learning_and_random_forests.ipynb
文件 5760930 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 8_dimensionality_reduction.ipynb
文件 203686 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 9_up_and_running_with_tensorflow.ipynb
文件 320538 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据10_introduction_to_artificial_neural_networks.ipynb
文件 1954346 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据11_deep_learning.ipynb
文件 24726 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据12_distributed_tensorflow.ipynb
文件 4981104 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据13_convolutional_neural_networks.ipynb
文件 674734 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据14_recurrent_neural_networks.ipynb
文件 349978 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据15_autoencoders.ipynb
文件 1415260 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据16_reinforcement_learning.ipynb
文件 48616 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据ook_equations.ipynb
文件 1423529 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetshousinghousing.csv
文件 409488 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetshousinghousing.tgz
文件 3680 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetshousingREADME.md
文件 31674 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetsinceptionimagenet_class_names.txt
文件 36323 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetslifesatgdp_per_capita.csv
文件 405467 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetslifesatoecd_bli_2015.csv
文件 4311 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetslifesatREADME.md
文件 32 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据docker.env
文件 89 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据dockerashrc.bash
文件 4986 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据dockerin
bclean_checkpoints
文件 583 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据dockerin
bdiff_checkpoint
文件 2136 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据dockerin
m_empty_subdirs
............此处省略71个文件信息
“““
This module merges two files from Scikit-Learn 0.20 to make a few encoders
available for users using an earlier version:
* sklearn/preprocessing/data.py (OneHotEncoder and CategoricalEncoder)
* sklearn/compose/_column_transformer.py (ColumnTransformer)
I just copy/pasted the contents fixed the imports and __all__ and also
copied the definitions of three pipeline functions whose signature changes
in 0.20: _fit_one_transformer _transform_one and _fit_transform_one.
The original authors are listed below.
----
The :mod:‘sklearn.compose._column_transformer‘ module implements utilities
to work with heterogeneous data and to apply different transformers to
different columns.
“““
# Authors: Andreas Mueller
# Joris Van den Bossche
# License: BSD 3 clause
from __future__ import division
import numbers
import warnings
import numpy as np
from scipy import sparse
from sklearn.base import clone baseEstimator TransformerMixin
from sklearn.externals import six
from sklearn.utils import Bunch check_array
from sklearn.externals.joblib.parallel import delayed Parallel
from sklearn.utils.metaestimators import _baseComposition
from sklearn.utils.validation import check_is_fitted FLOAT_DTYPES
from sklearn.pipeline import _name_estimators
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing.label import LabelEncoder
from itertools import chain
# weight and fit_params are not used but it allows _fit_one_transformer
# _transform_one and _fit_transform_one to have the same signature to
# factorize the code in ColumnTransformer
def _fit_one_transformer(transformer X y weight=None **fit_params):
return transformer.fit(X y)
def _transform_one(transformer X y weight **fit_params):
res = transformer.transform(X)
# if we have a weight for this transformer multiply output
if weight is None:
return res
return res * weight
def _fit_transform_one(transformer X y weight **fit_params):
if hasattr(transformer ‘fit_transform‘):
res = transformer.fit_transform(X y **fit_params)
else:
res = transformer.fit(X y **fit_params).transform(X)
# if we have a weight for this transformer multiply output
if weight is None:
return res transformer
return res * weight transformer
BOUNDS_THRESHOLD = 1e-7
zip = six.moves.zip
map = six.moves.map
range = six.moves.range
__all__ = [
‘OneHotEncoder‘
‘OrdinalEncoder‘
‘ColumnTransformer‘
‘make_column_transformer‘
]
def _argmax(arr_or_spmatrix axis=None):
return arr_or_spmatrix.argmax(axis=axis)
def _handle_zeros_in_scale(scale copy=True):
‘‘‘ Makes sure that whenever scale is zero we handle it correctly.
This happens in most scalers when we have constant features.‘‘‘
# if we are fitting on 1D arrays scale might be a scalar
if np.isscalar(scale):
if scale == .0:
scale
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 195 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据.gitignore
文件 284756 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 1_the_machine_learning_landscape.ipynb
文件 1350273 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 2_end_to_end_machine_learning_project.ipynb
文件 447511 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 3_classification.ipynb
文件 851389 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 4_training_linear_models.ipynb
文件 914359 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 5_support_vector_machines.ipynb
文件 204214 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 6_decision_trees.ipynb
文件 553305 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 7_ensemble_learning_and_random_forests.ipynb
文件 5760930 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 8_dimensionality_reduction.ipynb
文件 203686 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据 9_up_and_running_with_tensorflow.ipynb
文件 320538 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据10_introduction_to_artificial_neural_networks.ipynb
文件 1954346 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据11_deep_learning.ipynb
文件 24726 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据12_distributed_tensorflow.ipynb
文件 4981104 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据13_convolutional_neural_networks.ipynb
文件 674734 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据14_recurrent_neural_networks.ipynb
文件 349978 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据15_autoencoders.ipynb
文件 1415260 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据16_reinforcement_learning.ipynb
文件 48616 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据ook_equations.ipynb
文件 1423529 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetshousinghousing.csv
文件 409488 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetshousinghousing.tgz
文件 3680 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetshousingREADME.md
文件 31674 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetsinceptionimagenet_class_names.txt
文件 36323 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetslifesatgdp_per_capita.csv
文件 405467 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetslifesatoecd_bli_2015.csv
文件 4311 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据datasetslifesatREADME.md
文件 32 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据docker.env
文件 89 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据dockerashrc.bash
文件 4986 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据dockerin
bclean_checkpoints
文件 583 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据dockerin
bdiff_checkpoint
文件 2136 2018-09-17 17:51 Hands-On Machine Learning with Scikit-Learn & TensorFlow-master-源码+数据dockerin
m_empty_subdirs
............此处省略71个文件信息
版权声明:本文内容由互联网用户自发贡献,该文观点仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌抄袭侵权/违法违规的内容, 请发送邮件举报,一经查实,本站将立刻删除。
评论列表(条)