numpy.ravel — NumPy v2.3 Manual
numpy.ravel# numpy.ravel(a, order='C')[source]# Return a contiguous flattened array. A 1-D array, containing the elements of the input, is returned. A copy is made only if needed. As of NumPy 1.10, the returned array will have the same type as the input array. (for example, a masked array will be returned for a masked array input) Parameters: aarray_likeInput array. The elements in a are read in t
numpy.corrcoef — NumPy v2.3 Manual
numpy.corrcoef# numpy.corrcoef(x, y=None, rowvar=True, bias=<no value>, ddof=<no value>, *, dtype=None)[source]# Return Pearson product-moment correlation coefficients. Please refer to the documentation for cov for more detail. The relationship between the correlation coefficient matrix, R, and the covariance matrix, C, is The values of R are between -1 and 1, inclusive. Parameters: xarray_likeA 1
3.3. Scikit-image: 画像処理 — Scipy lecture notes
3.3. Scikit-image: 画像処理¶ 著者: Emmanuelle Gouillart scikit-image は画像処理に特化した Python 画像ライブラリで、 NumPy 配列を画像オブジェクトをネイティブに扱います。この章では scikit-image を多様な画像処理タスクにどう利用するかや NumPy や Scipy などの他の Python の科学技術モジュールとの連携についても扱います。 参考 基本的な画像操作、たとえば画像の切り抜きや単純なフィルタリングなど、多くの単純な操作は NumPy や SciPy でも実現できます Numpy と Scipy を利用した画像の操作と処理 を参照して下さい。 この章を読む前に前の章の内容について慣れておく必要があります、マスクやラベルといった基本操作は準備として必要です。
Anaconda Documentation - Anaconda
Whether you want to build data science/machine learning models, deploy your work to production, or securely manage a team of engineers, Anaconda provides the tools necessary to succeed. This documentation is designed to aid in building your understanding of Anaconda software and assist with any operations you may need to perform to manage your organization’s users and resources.
Kite is saying farewell - Code Faster with Kite
From 2014 to 2021, Kite was a startup using AI to help developers write code. We have stopped working on Kite, and are no longer supporting the Kite software. Thank you to everyone who used our product, and thank you to our team members and investors who made this journey possible. Our journey at Kite While we built next-generation experiences for developers, our business failed in two important w
Structured arrays — NumPy v2.3 Manual
Getting started What is NumPy? Installation NumPy quickstart NumPy: the absolute basics for beginners Fundamentals and usage NumPy fundamentals Array creation Indexing on ndarrays I/O with NumPy Data types Broadcasting Copies and views Working with Arrays of Strings And Bytes Structured arrays Universal functions (ufunc) basics NumPy for MATLAB users NumPy tutorials NumPy how-tos Advanced usage an
numpy.loadtxt — NumPy v2.3 Manual
numpy.loadtxt# numpy.loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding=None, max_rows=None, *, quotechar=None, like=None)[source]# Load data from a text file. Parameters: fnamefile, str, pathlib.Path, list of str, generatorFile, filename, list, or generator to read. If the filename extension is .gz or .bz2
numpy.logical_or — NumPy v2.3 Manual
numpy.logical_or# numpy.logical_or(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature]) = <ufunc 'logical_or'># Compute the truth value of x1 OR x2 element-wise. Parameters: x1, x2array_likeLogical OR is applied to the elements of x1 and x2. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the outp
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