pytorch.ipynb

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   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "import torch\n",
    "import torch.nn.functional as F"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [i for i in range()]"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
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     "data": {
      "text/plain": [
       "tensor([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0]])"
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "F.one_hot(torch.tensor([1]),50 )"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.8562, 0.3878])"
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "a = torch.rand(5)\n",
    "a[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "tensor([0.1675, 0.9535, 0.4283, 0.4480, 0.1068])"
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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    "a"
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  {
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   "metadata": {},
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   "source": [
    "a = 5"
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    {
     "data": {
      "text/plain": [
       "1"
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     "execution_count": 16,
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  {
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   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function Tensor.type>"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "torch.ones(2,2)"
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   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.randint(0, 100, (4,))"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(5)\n",
      "tensor(89)\n",
      "tensor(25)\n",
      "tensor(16)\n"
     ]
    }
   ],
   "source": [
    "for i in a:\n",
    "    print(i)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "a = torch.nn.Embedding(2, 4)"
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "data": {
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       "tensor([[-0.0814, -0.0782, -0.2089,  0.7043],\n",
       "        [ 0.3737,  0.7720,  1.2135, -0.7544]])"
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "a.weight.data"
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  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.3737,  0.7720,  1.2135, -0.7544]], grad_fn=<EmbeddingBackward0>)"
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     "execution_count": 4,
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   "source": [
    "a(torch.tensor([1]))"
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   "cell_type": "code",
   "execution_count": 15,
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   "outputs": [
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     "data": {
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       "tensor([[-0.7955+0.0000j, -1.7991-0.9850j, -0.0269+0.0000j, -1.7991+0.9850j],\n",
       "        [-0.5374+0.0000j,  3.6832-0.5162j,  2.4251+0.0000j,  3.6832+0.5162j]])"
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     "execution_count": 15,
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   "source": [
    "\n",
    "weight = a.weight.data\n",
    "torch.fft.fft(weight, dim= 1)"
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   "execution_count": 69,
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   "source": [
    "seq = torch.tensor([1,2,3,1,2,3,1,2,3])\n",
    "f = torch.fft.fft(seq)\n"
   ]
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   "cell_type": "code",
   "execution_count": 70,
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    "f"
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      "/var/folders/_j/ctxbrk0926zg7z8n7jc3xpp00000gn/T/ipykernel_46947/2081326806.py:2: UserWarning:\n",
      "\n",
      "torch.range is deprecated and will be removed in a future release because its behavior is inconsistent with Python's range builtin. Instead, use torch.arange, which produces values in [start, end).\n",
      "\n"
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          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "seq = torch.range(0,10)\n",
    "f = torch.fft.fft(seq, dim= 0)\n",
    "\n",
    "magnitudes = torch.abs(f).numpy()\n",
    "# Compute magnitudes\n",
    "\n",
    "# Only consider the first N/2 + 1 magnitudes\n",
    "half_n = len(seq) // 2 + 1\n",
    "magnitudes = magnitudes[:half_n]\n",
    "\n",
    "# Plotting\n",
    "fig = go.Figure()\n",
    "\n",
    "# Line plot for the original sequence\n",
    "# fig.add_trace(go.Scatter(y=seq.numpy(), mode='lines+markers', name='Original Sequence'))\n",
    "\n",
    "# Histogram for FFT magnitudes\n",
    "fig.add_trace(go.Bar(x=list(range(1, len(magnitudes))), y=magnitudes, name='FFT Magnitudes'))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([100, 100])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.tensor([[1,2,3],[4,5,6], [1,7,3], [4,2,6], [1,5,3], [4,7,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = torch.fft.fft(a,dim= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.5000e+01+0.0000e+00j,  2.8000e+01+0.0000e+00j,\n",
       "          2.7000e+01+0.0000e+00j],\n",
       "        [ 0.0000e+00+0.0000e+00j, -2.3842e-07-1.1921e-07j,\n",
       "          0.0000e+00+0.0000e+00j],\n",
       "        [ 0.0000e+00+0.0000e+00j, -8.0000e+00+3.4641e+00j,\n",
       "          0.0000e+00+0.0000e+00j],\n",
       "        [-9.0000e+00+0.0000e+00j,  0.0000e+00+0.0000e+00j,\n",
       "         -9.0000e+00+0.0000e+00j],\n",
       "        [ 0.0000e+00-0.0000e+00j, -8.0000e+00-3.4641e+00j,\n",
       "          0.0000e+00-0.0000e+00j],\n",
       "        [ 0.0000e+00-0.0000e+00j, -2.3842e-07+1.1921e-07j,\n",
       "          0.0000e+00-0.0000e+00j]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.717798233032227"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.norm(f[2]).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import math\n",
    "import torch.fft as fft\n",
    "import torch\n",
    "\n",
    "theta = 0\n",
    "data = []\n",
    "while(theta<256):\n",
    "    data.append(math.sin(math.radians(theta)), math.cos(math.radians(theta)))\n",
    "    theta+=15\n",
    "\n",
    "f = fft.fft(torch.tensor(x))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sin(math.radians(90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.7588+0.j)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.7615+0.1387j)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4.2979, 7.8833, 1.6147, 0.9556, 0.7068, 0.5797, 0.5067, 0.4640, 0.4413,\n",
       "        0.4342, 0.4413, 0.4640, 0.5067, 0.5797, 0.7068, 0.9556, 1.6147, 7.8833])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.return_types.max(\n",
       "values=tensor(7.8833),\n",
       "indices=tensor(1))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.abs().max(dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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