sparse

Scipy error with sparse hstack

匿名 (未验证) 提交于 2019-12-03 01:34:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: While attempting to combine dense and sparse data with scipy.spare.hstack, I'm occasionally running into the error: Traceback (most recent call last): File "hstack_error.py", line 3, in <module> X = scipy.sparse.hstack(hstack_parts) File "/usr/lib/python2.7/dist-packages/scipy/sparse/construct.py", line 263, in hstack return bmat([blocks], format=format, dtype=dtype) File "/usr/lib/python2.7/dist-packages/scipy/sparse/construct.py", line 329, in bmat raise ValueError('blocks must have rank 2') ValueError: blocks must have rank 2 Minimal code

Error importing scipy.sparse._sparsetools

匿名 (未验证) 提交于 2019-12-03 01:34:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: I'm working on a macbook pro trying to use the gensim package to do Word2Vec. I had used the model earlier in the week, but when I tried to resume using it, I was given an error regarding loading scipy. I tried loading only the specific model echoing the error message, and this is the same output: >>> import scipy . sparse . _sparsetools Traceback ( most recent call last ): File "<stdin>" , line 1 , in <module> File "//anaconda/lib/python2.7/site-packages/scipy/sparse/__init__.py" , line 213 , in <module> from . csr import * File "

Convert Sparse Vector to Dense Vector in Pyspark

匿名 (未验证) 提交于 2019-12-03 01:22:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: I have a sparse vector like this >>> countVectors.rdd.map(lambda vector: vector[1]).collect() [SparseVector(13, {0: 1.0, 2: 1.0, 3: 1.0, 6: 1.0, 8: 1.0, 9: 1.0, 10: 1.0, 12: 1.0}), SparseVector(13, {0: 1.0, 1: 1.0, 2: 1.0, 4: 1.0}), SparseVector(13, {0: 1.0, 1: 1.0, 3: 1.0, 4: 1.0, 7: 1.0}), SparseVector(13, {1: 1.0, 2: 1.0, 5: 1.0, 11: 1.0})] I am trying to convert this into dense vector in pyspark 2.0.0 like this >>> frequencyVectors = countVectors.rdd.map(lambda vector: vector[1]) >>> frequencyVectors.map(lambda vector: Vectors.dense

import scipy.sparse failed

匿名 (未验证) 提交于 2019-12-03 00:59:01
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: My python is 3.5.1 (32-bit) at Win7 (32-bit). I've installed 'scipy-0.16.1-cp35-none-win32.whl' and 'numpy-1.10.2+mkl-cp35-none-win32.whl' downloaded form http://www.lfd.uci.edu/~gohlke/pythonlibs/ . And the scipy model can be imported correctly as below: import scipy But I get the errors when importing scipy.sparse: >>> import scipy.sparse Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35-32\lib\site-packages\scipy\sparse\__init__.py", line 213, in

ImportError: DLL load failed: The specified module could not be found (sklearn)

匿名 (未验证) 提交于 2019-12-03 00:44:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: I installed numpy, scipy and scikit-learn in Python 3.5. When I run my program, I get this error. Traceback (most recent call last): File "D:/Dropbox/Cong Toan/Tools/NLP_Tools/doc_sim.py", line 1, in <module> from sklearn.feature_extraction.text import TfidfVectorizer File "C:\Python35-32\lib\site-packages\sklearn\__init__.py", line 57, in <module> from .base import clone File "C:\Python35-32\lib\site-packages\sklearn\base.py", line 11, in <module> from .utils.fixes import signature File "C:\Python35-32\lib\site-packages\sklearn\utils\__init

python机器学习入门到精通(一)

匿名 (未验证) 提交于 2019-12-02 22:54:36
机器学习,顾名思义,它是人工智能、统计学、计算机科学交叉的研究领域,也被称作预测分析或统计学习。近年来,由于人工智能的火爆,机器学习方法已经应用到日常生活的方方面面。贴近我们生活的就有,系统自动推荐用户的喜好,买什么物品,点什么食物,浏览新闻时,给你推送什么类型的文章,甚至于我们登录淘宝购买物品,微博观看别人的微博时,这些每一部分都可能包含多种机器学习模型。当然,机器学习也在科研方面也有建树,比如DNA分析,天文学的研究,微观粒子等。同时,要想学好机器学习,数学的基础也是必须的。 机器学算法是将决策过程自动化的算法,而这些决策是从已知示例中泛化得出来的,而这种方法被称作监督学习(supervised learning)。 用户将成对的输入和预期输出提供给算法,然后算法会找到一种方法,根据给定输入给出预期输出。从输入/输出对中进行学习的机器学习算法叫作监督学习算法,因为每个用于算法学习的样例都对应一个预期输出,就像老师教导学生在督算着算法。在生活中,监督机器学习任务大多应用于: 识别信封上手写的邮政编码, 输入扫描的手写数字,预期输出是邮政编码中的实际数字。想要创建用于构建机器学习模型的数据集,需要收集许多信封,然后可以自己阅读邮政编码,将数字保存为预期输出。 基于医学影像判断肿瘤是否为良性, 这里的输入是影像,输出是肿瘤是否为良性。创建用于构建模型的数据集,需要一个医学影像数据库

Sparse PCA 稀疏主成分分析

旧城冷巷雨未停 提交于 2019-12-01 01:25:42
Sparse PCA 稀疏主成分分析 2016-12-06 16:58:38 qilin2016 阅读数 15677 文章标签: 统计学习算法 更多 分类专栏: Machine Learning 版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。 本文链接: https://blog.csdn.net/zhoudi2010/article/details/53489319 SPCA原始文献: H. Zou (2006) Sparse principal component analysis PCA 可以参考: The Elements of Statistical Learning 第十四章 主成分分析的基本思想以及R的应用可以参考: 稀疏主成分分析与R应用 关于统计学习中的稀疏算法可以参考:Statistical learning with sparsity: the lasso and generalizations 一份很好的文档: http://www.cs.utexas.edu/~rashish/sparse_pca.pdf 首先直接来看算法: 令A初始化为V[,1:k],即为前k个principal components的loading vectors. 对于给定的 A = [ α 1 , … , α k ] A=