{"id":71829,"date":"2018-09-11T15:15:59","date_gmt":"2018-09-11T06:15:59","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=71829"},"modified":"2024-05-06T11:51:45","modified_gmt":"2024-05-06T02:51:45","slug":"%e3%80%90numpy%e5%85%a5%e9%96%80-np-matrix%e3%80%91","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/71829","title":{"rendered":"\u3010NumPy\u5165\u9580 np.matrix\u3011\u884c\u5217\u3092\u6271\u3046np.matrix\u30af\u30e9\u30b9\u306e\u7279\u6027\u3092\u77e5\u308d\u3046!"},"content":{"rendered":"

\u3053\u3093\u306b\u3061\u306f\u3001\u30a4\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u30fc\u306e\u30d5\u30af\u30ed\u30a6<\/a>\u3067\u3059\uff01<\/p>\n

\u3053\u306e\u8a18\u4e8b\u3067\u306f\u884c\u5217\u3092\u6271\u3046\u30af\u30e9\u30b9<\/strong><\/span>\u3001np.matrix<\/strong><\/span>\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\uff01<\/p>\n

NumPy\u3067\u306fnp.ndarray\u30af\u30e9\u30b9\u3067\u914d\u5217\u3092\u7ba1\u7406\u3057\u3066\u3044\u307e\u3057\u305f\u3002<\/p>\n

\u3053\u308c\u306b\u5bfe\u3057\u3066\u3001np.ndarray\u306e\u4e8c\u6b21\u5143\u914d\u5217\u306b\u5f53\u305f\u308b\u69cb\u9020\u306b\u306f\u7279\u5225\u306bnp.matrix\u30af\u30e9\u30b9\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n

np.matrix\u306f\u307b\u3068\u3093\u3069np.ndarray\u306e\u4e8c\u6b21\u5143\u914d\u5217\u9650\u5b9a\u7248<\/strong><\/span>\u3068\u3044\u3063\u3066\u3082\u826f\u3044\u306e\u3067\u3059\u304c\u3001\u5b9f\u306f\u5c11\u3057\u9055\u3046\u6a5f\u80fd\u3092\u6301\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n

\u3053\u306e\u8a18\u4e8b\u3067np.matrix\u306b\u3064\u3044\u3066\u8a73\u3057\u304f\u89e3\u8aac\u3059\u308b\u306e\u3067\u3001\u662f\u975e\u8aad\u3093\u3067\u304f\u3060\u3055\u3044\u306d\uff01<\/p>\n

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np.matrix\u3068\u306f<\/h2>\n<\/div>\n<\/div>\n<\/div>\n
Matrix\u306f\u884c\u5217\u3092\u8868\u3059\u30af\u30e9\u30b9\u3067\u3059\u3002<\/div>\n<\/div>\n
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\u3053\u306e\u30af\u30e9\u30b9\u306e\u30b3\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\uff08\u521d\u671f\u5316\u3059\u308b\u95a2\u6570\uff09\u306f\u3001np.matrix\/np.mat<\/strong><\/span>\u3068\u3044\u3046\uff12\u3064\u306e\u95a2\u6570\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

\u3069\u3061\u3089\u3092\u4f7f\u3063\u3066\u3082\u540c\u3058\u3082\u306e\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

import numpy as np\r\n\r\nX = np.matrix(\r\n    [\r\n        [1,2,3],\r\n        [4,5,6]\r\n    ]\r\n)\r\n\r\nX2 = np.mat(\r\n    [\r\n        [1,2,3],\r\n        [4,5,6]\r\n    ]\r\n)\r\n\r\nX3 = np.array(\r\n    [\r\n        [1,2,3],\r\n        [4,5,6]\r\n    ]\r\n)\r\n\r\nprint(type(X))\r\nprint(type(X2))\r\nprint(type(X3))<\/pre>\n
[\u51fa\u529b\u7d50\u679c]<\/p>\n
<class 'numpy.matrixlib.defmatrix.matrix'>\r\n<class 'numpy.matrixlib.defmatrix.matrix'>\r\n<class 'numpy.ndarray'><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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\u3053\u306e\u30af\u30e9\u30b9\u306f\u884c\u5217\u3092\u8868\u73fe\u3059\u308b\u3082\u306e\u306a\u306e\u3067\u3001\u4e8c\u6b21\u5143\u4ee5\u5916\u306e\u914d\u5217\u304b\u3089\u4f5c\u308d\u3046\u3068\u3059\u308b\u3068ValueError\u306b\u306a\u308a\u307e\u3059\u3002\u6ce8\u610f\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
X = np.matrix(\r\n    [[[1,2,3],[4,5,6]]]\r\n)<\/pre>\n
[\u30a8\u30e9\u30fc\u30e1\u30c3\u30bb\u30fc\u30b8]<\/p>\n
---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-19-bf08575410de> in <module>()\r\n      1 X = np.matrix(\r\n----> 2     [[[1,2,3],[4,5,6]]]\r\n      3 )\r\n\r\n~\/anaconda3\/lib\/python3.7\/site-packages\/numpy\/matrixlib\/defmatrix.py in __new__(subtype, data, dtype, copy)\r\n    144         shape = arr.shape\r\n    145         if (ndim > 2):\r\n--> 146             raise ValueError(\"matrix must be 2-dimensional\")\r\n    147         elif ndim == 0:\r\n    148             shape = (1, 1)\r\n\r\nValueError: matrix must be 2-dimensional\r\n<\/pre>\n
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np.matrix\u3068np.array\u306e\u52d5\u4f5c\u306e\u9055\u3044<\/h2>\n
np.matrix\u3068np.ndarray\u306f\u307b\u3068\u3093\u3069\u540c\u3058\u52d5\u4f5c\u3092\u3059\u308b\u3001\u3053\u308c\u306f\u5148\u7a0b\u66f8\u304d\u307e\u3057\u305f\u306d\u3002<\/p>\n
\u5b9f\u306f\u3053\u308c\u3089\u304c\u9055\u3046\u52d5\u304d\u3092\u3059\u308b\u5834\u5408\u304c\u3044\u304f\u3064\u304b\u3042\u308a\u307e\u3059<\/strong><\/span>\u3002<\/p>\n
\u305d\u308c\u3089\u306e\u4e2d\u3067\u3082\u7279\u306b\u4e3b\u8981\u306a\u3082\u306e\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n
\u5185\u7a4d<\/h3>\n
\u914d\u5217\u540c\u58eb\u306e\u5185\u7a4d<\/strong><\/span>\u3092\u53d6\u308b\u8a08\u7b97\u306b\u3001np.dot<\/strong><\/span>\u304c\u3042\u308a\u307e\u3057\u305f\u3002<\/p>\n            \n                
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\n                    
\n                    \n                    <\/div>\n                    
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\u3010NumPy\u5165\u9580 np.dot\u3011\u884c\u5217\u8a08\u7b97\u306e\u57fa\u790e\uff01np.dot\u3067\u306e\u5185\u7a4d\u8a08\u7b97\u306e\u4ed5\u65b9\uff01<\/div>\n                    
\u66f4\u65b0\u65e5\uff1a2024\u5e745\u67086\u65e5<\/div>\n                    <\/div>\n                <\/div>\n                <\/div>\n            <\/a>\n            \n
\u3053\u306e\u95a2\u6570\u306f\u3082\u3061\u308d\u3093\u3001np.ndarray\u3067\u3082np.matrix\u3067\u3082\u4f7f\u3048\u307e\u3059\u3002<\/p>\n
\u3067\u3059\u304c\u3001np.ndarray\u3067\u306f*\u6f14\u7b97\u5b50\u3067\u306f\u5185\u7a4d\u8a08\u7b97\u306f\u3067\u304d\u306a\u3044<\/strong><\/span>\u306e\u306b\u5bfe\u3057\u3066\u3001np.matrix\u3067\u306f*\u6f14\u7b97\u5b50\u3067\u5185\u7a4d\u304c\u8a08\u7b97\u3067\u304d\u307e\u3059<\/strong><\/span>\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n
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np.matrix\u306e\u5185\u7a4d\u8a08\u7b97<\/h4>\n
X*X.transpose()\r\n\r\n# \u8a08\u7b97\u7d50\u679c\r\nmatrix([[14, 32],\r\n        [32, 77]])\r\n\r\n<\/pre>\n
np.dot(X,X.transpose())\r\n\r\n# \u8a08\u7b97\u7d50\u679c\r\nmatrix([[14, 32],\r\n        [32, 77]])<\/pre>\n
np.array\u306e\u5185\u7a4d\u8a08\u7b97<\/h4>\n
np.dot(X3,X3.transpose())\r\n\r\n# \u8a08\u7b97\u7d50\u679c\r\narray([[14, 32],\r\n       [32, 77]])<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
X3*X3.transpose()<\/pre>\n
[\u30a8\u30e9\u30fc\u30e1\u30c3\u30bb\u30fc\u30b8]<\/p>\n
---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-32-0720f2ef1dc3> in <module>()\r\n----> 1 X3*X3.transpose()\r\n\r\nValueError: operands could not be broadcast together with shapes (2,3) (3,2) \r\n<\/pre>\n
\u30a2\u30c0\u30de\u30fc\u30eb\u7a4d\uff08\u8981\u7d20\u3054\u3068\u306e\u7a4d\uff09<\/h3>\n
\u914d\u5217\u306e\u8981\u7d20\u3054\u3068\u306e\u7a4d<\/strong><\/span>\u3092\u8a08\u7b97\u3059\u308b\u306b\u306f\u3001np.array\u3067\u306f*\u6f14\u7b97\u5b50\u3067\u53ef\u80fd<\/strong><\/span>\u3067\u3059\u304c\u3001np.matrix\u3067\u306f\u3067\u304d\u307e\u305b\u3093<\/span><\/strong>\u3002<\/p>\n
np.matrix\u3067\u306fnp.multiply\u95a2\u6570<\/strong><\/span>\u3092\u4f7f\u3044\u307e\u3059\u3002\uff08np.arary\u3067\u3082np.multiply\u306f\u4f7f\u3048\u307e\u3059\u3088\uff09<\/p>\n
np.matrix\u306e\u30a2\u30c0\u30de\u30fc\u30eb\u7a4d<\/h4>\n
np.matrix\u540c\u58eb\u3092*\u6f14\u7b97\u5b50\u3067\u8a08\u7b97\u3057\u3088\u3046\u3068\u3059\u308b\u3068\u30a8\u30e9\u30fc\u304c\u3067\u307e\u3059\u3002<\/p>\n
X*X<\/pre>\n
[\u30a8\u30e9\u30fc\u30e1\u30c3\u30bb\u30fc\u30b8]<\/p>\n
---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-34-0794a8b26aec> in <module>()\r\n----> 1 X*X\r\n\r\n~\/anaconda3\/lib\/python3.7\/site-packages\/numpy\/matrixlib\/defmatrix.py in __mul__(self, other)\r\n    213         if isinstance(other, (N.ndarray, list, tuple)) :\r\n    214             # This promotes 1-D vectors to row vectors\r\n--> 215             return N.dot(self, asmatrix(other))\r\n    216         if isscalar(other) or not hasattr(other, '__rmul__') :\r\n    217             return N.dot(self, other)\r\n\r\nValueError: shapes (2,3) and (2,3) not aligned: 3 (dim 1) != 2 (dim 0)<\/pre>\n
\u30a2\u30c0\u30de\u30fc\u30eb\u7a4d\u3092\u8a08\u7b97\u3059\u308b\u306a\u3089np.ndarray.multiply\u95a2\u6570\u3092\u4f7f\u3048\u3070OK\u3067\u3059\u3002<\/p>\n
np.multiply(X,X)\r\n\r\n# \u8a08\u7b97\u7d50\u679c\r\nmatrix([[ 1,  4,  9],\r\n        [16, 25, 36]])\r\n<\/pre>\n
np.array\u306e\u30a2\u30c0\u30de\u30fc\u30eb\u7a4d<\/h4>\n
np.ndarray\u540c\u58eb\u306a\u3089\u3070*\u6f14\u7b97\u5b50\u3067\u3082\u30a2\u30c0\u30de\u30fc\u30eb\u7a4d\u304c\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/p>\n
X3*X3\r\n\r\n# \u8a08\u7b97\u7d50\u679c\r\narray([[ 1,  4,  9],\r\n       [16, 25, 36]])<\/pre>\n
\u307e\u305f\u3001np.ndarray.multiply\u95a2\u6570\u306a\u3089\u3070\u3001np.ndarray\u3067\u3082np.matrix\u3067\u3082\u540c\u69d8\u306b\u30a2\u30c0\u30de\u30fc\u30eb\u7a4d\u304c\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/p>\n
np.multiply(X3,X3)\r\n\r\n# \u8a08\u7b97\u7d50\u679c\r\narray([[ 1,  4,  9],\r\n       [16, 25, 36]])<\/pre>\n
\u307e\u3068\u3081<\/h2>\n
\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001NumPy\u3067\u884c\u5217\u3092\u6271\u3046\u30af\u30e9\u30b9np.matrix\u306e\u7d39\u4ecb<\/strong><\/span>\u3068\u3001np.ndarray\u3068\u306e\u52d5\u4f5c\u306e\u9055\u3044\u306b\u3064\u3044\u3066\u89e3\u8aac<\/strong><\/span>\u3057\u307e\u3057\u305f\u3002<\/p>\n
np.matrix\u3092\u4f7f\u3048\u3070\u660e\u793a\u7684\u306b\u884c\u5217\u3092\u6271\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u884c\u5217\u8a08\u7b97\u306f\u3069\u3093\u306a\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u5b9f\u88c5\u3067\u3082\u3088\u304f\u4f7f\u308f\u308c\u308b\u3082\u306e\u3067\u3059\u3002<\/p>\n
np.matrix\u306e\u4f7f\u3044\u65b9\u3092\u899a\u3048\u3066\u3001\u662f\u975e\u3068\u3082\u52b9\u7387\u7684\u306b\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u3092\u884c\u3044\u307e\u3057\u3087\u3046\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"
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