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aktech/symmetric_matrix.ipynb

Created November 9, 2020 21:28
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Symmetric Matrix Dask
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symmetric_matrix.ipynb
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import dask.array as da\n",
"from dask.diagnostics import ProgressBar, ResourceProfiler\n",
"from bokeh.io import output_notebook\n",
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
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"</svg>\n",
"</td>\n",
"</tr>\n",
"</table>"
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"text/plain": [
"dask.array<randint, shape=(100000, 100000), dtype=int8, chunksize=(10000, 10000), chunktype=numpy.ndarray>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rs = da.random.RandomState(0)\n",
"x = rs.randint(0, 3, size=(100000, 100000), dtype='i1',)\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"dask.array<add, shape=(100000, 100000), dtype=int8, chunksize=(10000, 10000), chunktype=numpy.ndarray>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_ = da.triu(x, 1) + da.triu(x).T\n",
"x_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Compute by making lower triangular matrix 0"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[########################################] | 100% Completed | 47.4s\n",
"CPU times: user 4min 41s, sys: 1min 25s, total: 6min 6s\n",
"Wall time: 2min 21s\n"
]
}
],
"source": [
"%%time\n",
"with ProgressBar(), ResourceProfiler() as prof:\n",
" k = x_.compute()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Compute full matrix"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[########################################] | 100% Completed | 18.1s\n",
"CPU times: user 1min 50s, sys: 42.1 s, total: 2min 32s\n",
"Wall time: 1min 39s\n"
]
}
],
"source": [
"%%time\n",
"with ProgressBar(), ResourceProfiler() as prof:\n",
" k = x.compute()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"name": "ipython",
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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