Functional approach to a Tensor implementation.

Functional.ipynb

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   "source": [
    "# Soy el usuario\n",
    "\n",
    "def sum_tensor(sub, sup):\n",
    "    return sum(sub) + sum(sup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Negated tensor\n",
    "\n",
    "def neg_sum_tensor(sub, sup):\n",
    "    return - sum_tensor(sub, sup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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   "source": [
    "def null_tensor(sub, sup):\n",
    "    return sum_tensor(sub, sup) + neg_sum_tensor(sub, sup)"
   ]
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   "source": [
    "null_tensor((1, 1, 1), ())"
   ]
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   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
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   "source": [
    "class Tensor(object):\n",
    "    \n",
    "    def __init__(self, shape, func):\n",
    "        self.shape = shape\n",
    "        self.func = func\n",
    "    \n",
    "    def __call__(self, sub, sup):\n",
    "        width, height = self.shape\n",
    "        if len(sub) != width or len(sup) != height:\n",
    "            raise ValueError('Wachaut!')\n",
    "        return self.func(sub, sup)\n",
    "        \n",
    "    def __add__(self, other):\n",
    "        if self.shape != other.shape:\n",
    "            raise ValueError('Shapes should match!')\n",
    "        return Tensor(self.shape, lambda sub, sup: self.func(sub, sup) + other.func(sub, sup))\n",
    "    \n",
    "    @classmethod\n",
    "    def from_values(cls, shape, values):\n",
    "         return cls(shape, lambda sub, sup: values.get((sub, sup), 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = Tensor((2, 2), sum_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
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       "4"
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     "execution_count": 25,
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   ],
   "source": [
    "t((1,1),(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = t + t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
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     "execution_count": 27,
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    "k((1,1),(1,1))"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_of_values = {\n",
    "    ((0,0), (0,1)): 3, # The first sumand\n",
    "    ((0,1), (1,3)): -2 # The second sumand\n",
    "}\n",
    "\n",
    "T = Tensor.from_values((2, 2), dict_of_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
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     "execution_count": 29,
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   ],
   "source": [
    "T((2,2),(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
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     "data": {
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       "3"
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     "execution_count": 31,
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   ],
   "source": [
    "T((0,0),(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "F = T + k\n"
   ]
  },
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   "execution_count": 34,
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    "F((0,0),(0,1))"
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   "execution_count": null,
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