my solution to the final exercise

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Tiziano Zito 2024-08-21 12:34:31 +02:00
parent 288e33f5b3
commit df68a1636d

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exercise-my-solution.ipynb Normal file
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"source": [
"import numpy as np"
]
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"n_series = 32\n",
"len_one_series = 5*2**20\n",
"time_series = np.random.rand(n_series, len_one_series)\n",
"gap = 16*2**10"
]
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"cell_type": "code",
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"source": [
"print(f'Size of one time series: {int(time_series[0].nbytes/2**20)} M')\n",
"print(f'Size of collection: {int(time_series.nbytes/2**20)} M')\n",
"print(f'Gap size: {int(gap*8/2**10)} K')\n",
"print(f'Gapped series size: {int(time_series[0, ::gap].nbytes/2**10)} K')"
]
},
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"source": [
"# compute a Taylor-like series\n",
"def taylor(time_series, mean, gap):\n",
" for row, ts in enumerate(time_series):\n",
" for pwr in range(1,20):\n",
" mean[row] += (ts[::gap]**pwr).sum()\n",
" return mean\n",
" "
]
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"source": [
"def taylor_improved(time_series, mean, gap):\n",
" y = time_series[:,::gap].copy()\n",
" for row, ts in enumerate(y):\n",
" for pwr in range(1,20):\n",
" mean[row] += (ts**pwr).sum()\n",
" return mean"
]
},
{
"cell_type": "code",
"execution_count": null,
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"source": [
"# verify that they yield the same results\n",
"out1 = taylor(time_series, np.zeros(n_series), gap)\n",
"out2 = taylor_improved(time_series, np.zeros(n_series), gap)\n",
"np.testing.assert_allclose(out1, out2)"
]
},
{
"cell_type": "code",
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"outputs": [],
"source": [
"mean = np.zeros(n_series, dtype='float64')\n",
"%timeit taylor(time_series, mean, gap)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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"outputs": [],
"source": [
"mean = np.zeros(n_series, dtype='float64')\n",
"%timeit taylor_improved(time_series, mean, gap)"
]
}
],
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