operator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.tensor([1,1],dtype = torch.float)\n",
    "y = torch.tensor([1,2],dtype = torch.float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1., 1.])\n",
      "tensor([[1.],\n",
      "        [1.]])\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(x.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2., 3.])\n",
      "tensor([ 0., -1.])\n",
      "tensor([1., 2.])\n",
      "tensor([1.0000, 0.5000])\n"
     ]
    }
   ],
   "source": [
    "print(x+y)\n",
    "print(x-y)\n",
    "print(x*y)\n",
    "print(x/y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.5000, 1.5000])\n"
     ]
    }
   ],
   "source": [
    "a = 1.5\n",
    "print(a*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.4142)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.norm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.dist(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = torch.tensor([[1,1],[0,1]],dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 2.],\n",
      "        [0., 2.]])\n",
      "tensor([[1., 2.],\n",
      "        [0., 2.]])\n"
     ]
    }
   ],
   "source": [
    "print(A*y)\n",
    "print(y*A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 1.],\n",
      "        [0., 2.]])\n",
      "tensor([[1., 1.],\n",
      "        [0., 2.]])\n"
     ]
    }
   ],
   "source": [
    "print(A*y.reshape(-1,1))\n",
    "print(y.reshape(-1,1)*A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[2.],\n",
      "        [1.]])\n"
     ]
    }
   ],
   "source": [
    "print(A.mm(x.reshape(-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1., -1.],\n",
      "        [ 0.,  1.]])\n",
      "tensor([[1., 0.],\n",
      "        [0., 1.]])\n"
     ]
    }
   ],
   "source": [
    "print(A.inverse())\n",
    "print(A.inverse().mm(A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.return_types.eig(\n",
      "eigenvalues=tensor([[1., 0.],\n",
      "        [1., 0.]]),\n",
      "eigenvectors=tensor([]))\n"
     ]
    }
   ],
   "source": [
    "# ジョルダン系なので\n",
    "print(A.eig())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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