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benchmark one_column + graph

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JernKunpittaya
2023-12-28 20:46:38 +07:00
parent f7c8674ef6
commit 8aaf77d4fb
30 changed files with 4131 additions and 1573 deletions
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1
bench.py
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@@ -199,6 +199,7 @@ def bench_one(data_path_array, model_func, gen_param_func, data_name, scale,mode
# create dummy part, not need to save dummy data part
dummy_data = np.round(np.random.uniform(1,10,len(data)),1)
# dummy_data = np.round(np.random.uniform(10,100,len(data)),0)*10
dummy_data_tensor_array.append(torch.reshape(torch.tensor(dummy_data), (1, len(dummy_data),1 )))
json.dump(dict(input_data = comb_data), open(comb_data_path, 'w' ))

857
benchmark/one_column/bench_geomean.ipynb
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benchmark/one_column/bench_mean.ipynb
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benchmark/one_column/bench_median.ipynb
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913
benchmark/one_column/bench_mode.ipynb
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benchmark/one_column/bench_stdev.ipynb
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11
benchmark/one_column/data00.json
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@@ -1,11 +0,0 @@
{
"input_data": [
[
0.95, 0.03, 0.83, 0.58, 0.26, 0.83, 0.46, 0.53, 0.38, 0.86, 0.54, 0.79,
0.12, 0.06, 0.69, 0.4, 0.07, 0.77, 0.23, 0.6, 0.91, 0.1, 0.69, 0.39, 0.46,
0.96, 0.36, 0.02, 0.77, 0.14, 0.68, 0.17, 0.66, 0.92, 0.71, 0.89, 0.81,
0.12, 0.04, 0.68, 0.46, 0.6, 0.02, 0.85, 0.65, 0.48, 0.08, 0.26, 0.43,
0.56
]
]
}

31
benchmark/one_column/data10.json
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@@ -1,31 +0,0 @@
{
"input_data": [
[
0.98, 0.61, 0.79, 0.14, 0.95, 0.39, 0.84, 0.24, 0.09, 0.36, 0.31, 0.09,
0.04, 0.06, 0.62, 0.07, 0.54, 0.82, 0.73, 0.43, 0.11, 0.67, 0.11, 0.98,
0.91, 0.38, 0.54, 0.12, 0.76, 0.21, 0.26, 0.39, 0.48, 0.16, 0.65, 0.07,
0.81, 0.85, 0.29, 0.19, 0.67, 0.15, 0.38, 0.61, 0.62, 0.23, 0.27, 0.12,
0.46, 0.86, 0.41, 0.9, 0.55, 0.59, 0.8, 0.29, 0.31, 0.75, 0.49, 0.99,
0.64, 0.41, 0.41, 0.5, 0.71, 0.3, 0.71, 0.24, 0.23, 0.71, 0.09, 0.86,
0.54, 0.46, 0.73, 0.12, 0.5, 0.68, 0.69, 0.93, 0.69, 0.02, 0.54, 0.3,
0.48, 0.54, 0.02, 0.47, 0.4, 0.63, 0.77, 0.3, 0.76, 0.34, 0.16, 0.93, 1.0,
0.22, 0.62, 0.66, 0.75, 0.08, 0.55, 0.45, 0.3, 0.63, 0.27, 0.89, 0.67,
0.59, 0.09, 0.62, 0.11, 0.57, 0.68, 0.33, 0.48, 0.16, 0.71, 0.36, 0.66,
0.59, 0.43, 0.83, 0.66, 0.74, 0.74, 0.75, 0.51, 0.38, 0.82, 0.71, 0.43,
0.97, 0.11, 0.93, 0.5, 0.62, 0.41, 0.56, 0.4, 0.23, 0.74, 0.36, 0.22,
0.73, 0.26, 0.61, 0.17, 0.39, 0.41, 0.8, 0.88, 0.32, 0.33, 0.29, 0.26,
0.45, 0.83, 0.32, 0.88, 0.64, 0.63, 0.27, 0.97, 0.43, 0.2, 0.2, 0.84,
0.76, 0.21, 0.22, 0.09, 0.9, 0.69, 0.3, 0.57, 0.03, 0.21, 0.29, 0.99,
0.06, 0.07, 0.37, 0.78, 0.91, 0.23, 0.95, 0.15, 0.57, 0.54, 0.68, 0.52,
0.82, 0.64, 0.18, 0.4, 0.23, 0.37, 0.6, 0.16, 0.23, 0.64, 0.77, 0.58,
0.13, 0.92, 0.14, 0.29, 0.67, 0.68, 0.52, 0.88, 0.16, 0.08, 0.91, 0.83,
0.17, 0.51, 0.83, 0.69, 0.19, 0.91, 0.76, 0.21, 0.24, 0.05, 0.23, 0.24,
0.91, 0.46, 0.9, 0.36, 0.22, 0.83, 0.98, 0.77, 0.6, 0.49, 0.8, 0.32, 0.01,
0.12, 0.82, 0.93, 0.44, 0.74, 0.44, 0.39, 0.87, 0.44, 0.97, 0.32, 0.73,
0.47, 0.97, 0.29, 0.59, 0.8, 0.16, 0.58, 0.15, 0.18, 0.27, 0.38, 0.08,
0.94, 0.47, 0.9, 0.29, 0.68, 0.03, 0.08, 0.76, 0.34, 0.48, 0.61, 0.35,
0.4, 0.71, 0.23, 0.18, 0.06, 0.2, 0.27, 0.41, 0.53, 0.74, 0.6, 0.18, 0.69,
0.62, 0.48, 0.39, 0.45, 0.76, 0.19, 0.63, 0.16, 0.13
]
]
}

89
benchmark/one_column/data20.json
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@@ -1,89 +0,0 @@
{
"input_data": [
[
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0.68, 0.7, 0.47, 0.13, 0.62, 0.68, 0.12, 0.42, 0.94, 0.14, 0.98, 0.64,
0.45, 0.3, 0.99, 0.91, 0.37, 0.36, 0.88, 0.22, 0.08, 0.84, 0.02, 0.63,
0.95, 0.25, 0.2, 0.66, 0.44, 0.24, 0.35, 0.16, 0.04, 0.21, 0.36, 0.71,
0.69, 0.51, 0.67, 0.39, 0.19, 0.65, 0.47, 0.56, 0.85, 0.54, 0.48, 0.41,
0.58, 0.76, 0.27, 0.74, 0.87, 0.33, 0.47, 0.25, 0.51, 0.38, 0.22, 0.73,
0.14, 0.18, 0.72, 0.13, 0.02, 0.79, 0.18, 0.3, 0.62, 0.41, 0.42, 0.12,
0.18, 0.35, 0.23, 0.92, 0.81, 0.65, 0.43, 0.95, 0.68, 0.13, 0.54, 0.28,
0.8, 0.14, 0.45, 0.5, 0.64, 0.77, 0.84, 0.36, 0.94, 0.59, 0.63, 0.28,
0.61, 0.8, 0.62, 0.2, 0.74, 0.99, 0.82, 0.83, 0.24, 0.83, 0.19, 0.02,
0.16, 0.51, 0.32, 0.48, 0.68, 0.25, 0.88, 0.5, 0.61, 0.66, 0.08, 0.9,
0.23, 0.4, 0.23, 0.7, 0.8, 0.09, 0.71, 0.98, 0.8, 0.42, 0.53, 0.9, 0.55,
0.4, 0.68, 0.09, 0.12, 0.32, 0.84, 0.24, 0.24, 0.49, 0.7, 0.65, 0.55,
0.11, 0.04, 0.71, 0.19, 0.64, 0.12, 0.02, 0.59, 0.89, 0.39, 0.68, 0.36,
0.85, 0.95, 0.63, 0.95, 0.42, 0.3, 0.95, 0.29, 0.08, 0.34, 0.06, 0.09,
0.97, 0.89, 0.39, 0.78, 0.03, 0.01, 0.54, 0.37, 0.07, 0.39, 0.41, 0.07,
0.69, 0.99, 0.7, 0.7, 0.68, 0.07, 0.88, 0.54, 0.26, 0.55, 0.14, 0.05,
0.57, 0.4, 0.32, 0.85, 0.88, 0.85, 0.69, 0.27, 0.4, 0.96, 0.15, 0.62,
0.14, 0.51, 0.35, 0.46, 0.99, 0.44, 0.46, 0.72, 0.71, 0.96, 0.87, 0.79,
0.1, 0.9, 0.17, 0.64, 0.55, 0.63, 0.56, 0.84, 0.76, 0.4, 0.99, 0.74, 0.04,
0.49, 0.99, 0.06, 0.03, 0.52, 0.73, 0.32, 0.37, 0.92, 0.85, 0.93, 0.05,
0.79, 0.11, 0.84, 0.09, 0.56, 0.73, 0.95, 0.48, 0.5, 0.97, 0.51, 0.36,
0.06, 0.16, 0.37, 0.7, 0.64, 0.52, 0.09, 0.41, 0.92, 0.17, 0.51, 0.98,
0.42, 0.86, 0.67, 0.47, 0.84, 0.41, 0.75, 0.34, 0.03, 0.06, 0.23, 0.89,
0.78, 0.71, 0.07, 0.96, 0.7, 0.95, 0.76, 0.2, 0.2, 0.08, 0.83, 0.77, 0.59,
0.87, 0.16, 0.87, 0.17, 0.42, 0.64, 0.34, 0.64, 0.7, 0.42, 0.85, 0.55,
0.77, 0.82, 0.23, 0.17, 0.75, 0.14, 0.39, 0.03, 0.93, 0.84, 0.73, 1.0,
0.64, 0.39, 0.45, 0.15, 1.0, 0.5, 0.64, 0.69, 0.19, 0.08, 0.41, 0.11,
0.22, 0.04, 0.27, 0.23, 0.1, 0.85, 0.38, 0.32, 0.21, 0.91, 0.72, 0.81,
0.93, 0.81, 0.46, 0.91, 0.24, 0.66, 0.08, 0.96, 0.38, 0.63, 0.65, 0.04,
0.51, 0.12, 0.27, 0.67, 0.15, 0.29, 0.06, 0.83, 0.11, 0.87, 0.28, 0.81,
0.47, 0.77, 0.54, 0.16, 0.27, 0.72, 0.34, 0.34, 0.33, 0.91, 0.89, 0.66,
0.54, 0.28, 0.77, 0.92, 0.42, 0.49, 0.33, 0.67, 0.65, 0.48, 0.05, 0.53,
0.89, 0.7, 0.91, 0.27, 0.04, 0.17, 0.73, 0.58, 0.4, 0.5, 0.05, 0.18, 0.59,
0.86, 0.16, 0.53, 0.22, 0.73, 0.34, 0.32, 0.93, 0.43, 0.77, 0.35, 0.8,
0.03, 0.8, 0.59, 0.85, 0.58, 0.55, 0.29, 0.63, 0.99, 0.43, 0.52, 0.1, 0.4,
0.11, 0.21, 0.26, 0.61, 0.04, 0.28, 0.25, 0.18, 0.37, 0.1, 0.85, 0.44,
0.95, 0.43, 0.98, 0.58, 0.83, 0.25, 0.87, 0.22, 0.4, 0.14, 0.14, 0.65,
0.4, 0.74, 0.62, 0.45, 0.14, 0.86, 0.6, 0.86, 0.98, 0.63, 0.52, 0.52, 0.8,
0.63, 0.82, 0.12, 0.61, 0.4, 0.96, 0.87, 0.34, 0.08, 0.43, 0.14, 0.3,
0.62, 0.86, 0.96, 0.32, 0.53, 0.02, 0.79, 0.55, 0.9, 0.19, 0.31, 0.82,
0.18, 0.06, 0.73, 0.67, 0.7, 0.62, 0.28, 0.61, 0.42, 0.74, 0.61, 0.87,
0.46, 0.86, 0.78, 0.91, 0.93, 0.26, 0.07, 0.11, 0.9, 0.83, 0.8, 0.22,
0.68, 0.13, 0.01, 0.47, 0.13, 0.56, 0.39, 0.28, 0.56, 0.3, 0.73, 0.03,
0.22, 0.27, 0.7, 0.92, 0.17, 0.74, 0.3, 0.33, 0.78, 0.76, 0.66, 0.77,
0.17, 0.81, 0.98, 0.33, 0.17, 0.29, 0.05, 0.16, 0.36, 0.05, 0.32, 0.91,
0.91, 0.35, 0.86, 0.46, 0.23, 0.39, 0.36, 0.52, 0.84, 0.58, 0.52, 1.0,
0.74, 0.58, 0.88, 0.4, 0.75, 0.57, 0.09, 0.66, 0.72, 0.37, 0.97, 0.52,
0.5, 0.55, 0.82, 0.59, 0.86, 0.89, 0.53, 0.72, 0.29, 0.06, 0.08, 0.26,
0.65, 0.62, 0.16, 0.27, 0.89, 0.31, 0.15, 0.77, 0.81, 0.09, 0.66, 0.91,
0.91, 0.59, 0.48, 0.97, 0.87, 0.81, 0.89, 0.88, 0.31, 0.47, 0.48, 0.35,
0.99, 0.62, 0.13, 0.55, 0.41, 1.0, 0.39, 0.45, 0.79, 0.09, 0.63, 0.5,
0.63, 0.55, 0.92, 0.49, 0.16, 0.61, 0.27, 0.52, 0.48, 0.6, 0.38, 0.56,
0.55, 0.39, 0.86, 0.25, 0.72, 0.62, 0.87, 0.24, 0.63, 0.16, 0.7, 0.21,
0.93, 0.22, 0.51, 0.28, 0.48, 0.85, 0.18, 0.63, 0.63, 0.15, 0.43, 0.47,
0.76, 0.69, 0.4, 0.12, 0.86, 0.84, 0.93, 0.77, 0.81, 0.46, 0.72, 0.96,
0.96, 0.71, 0.15, 0.26, 0.78, 0.21, 0.68, 0.01, 0.2, 0.68, 0.67, 0.42,
0.34, 0.17, 0.85, 0.99, 0.66, 0.29, 0.69, 0.3, 0.19, 0.72, 0.28, 0.29,
0.32, 0.88, 0.22, 0.9, 0.91, 0.04, 0.21, 0.91, 0.84, 0.14, 0.5, 0.64,
0.99, 0.59, 0.8, 0.87, 0.31, 0.45, 0.67, 0.28, 0.74, 0.98, 0.26, 0.42,
0.23, 0.88, 0.91, 0.89, 0.31, 0.87, 0.14, 0.53, 0.75, 0.53, 0.38, 0.44,
0.78, 0.58, 0.52, 0.07, 0.56, 0.62, 0.22, 0.02, 0.51, 0.92, 0.23, 0.52,
0.12, 0.07, 0.93, 0.52, 0.57, 0.59, 0.64, 0.67, 0.59, 0.97, 0.1, 0.41,
0.62, 0.4, 0.18, 0.44, 0.35, 0.51, 0.92, 0.74, 0.53, 0.48, 0.37, 0.32,
0.15, 0.34, 0.53, 0.3, 0.86, 0.33, 0.21, 0.31, 0.81, 0.36, 0.67, 0.29,
0.36, 0.42, 0.98, 0.51, 0.34, 0.65, 0.88, 0.86, 0.91, 0.44, 0.19, 0.25,
0.09, 0.43, 0.83, 0.09, 0.84, 0.37, 0.77, 0.24, 0.66, 0.79, 0.41, 0.68,
0.41, 0.61, 0.83, 0.74, 1.0, 0.9, 0.49, 0.62, 0.51, 0.58, 0.62, 0.62,
0.26, 0.32, 0.03, 0.39, 0.9, 0.56, 0.72, 0.25, 0.45, 0.07, 0.66, 0.12,
0.58, 0.81, 0.18, 0.66, 0.54, 0.18, 0.86, 0.83, 0.86, 0.58, 0.94, 0.81,
0.19, 0.9, 0.62, 0.8, 0.56, 0.46, 0.2, 0.55, 0.08, 0.24, 0.28, 0.79, 0.11,
0.01, 0.54, 0.19, 0.3, 0.85, 0.77, 0.3, 0.83, 0.57, 0.15, 0.75, 0.13,
0.67, 0.38, 0.79, 0.44, 0.08, 0.84, 0.93, 0.75, 0.61, 0.43, 0.88, 0.11,
0.57, 0.44, 0.71, 0.56, 0.23, 0.98, 0.09, 0.37, 0.98, 0.05, 0.26, 0.25,
0.08, 0.98, 0.61, 0.88, 0.39, 0.73, 0.73, 0.77, 0.19, 0.71, 0.92, 0.05,
0.09, 0.77, 0.47, 0.95, 0.32, 0.11, 0.75, 0.27, 0.44, 0.32, 0.7, 0.23,
0.21, 0.75, 0.78, 0.5, 0.88, 0.4, 0.84, 0.43, 0.51, 0.15, 0.97, 0.09,
0.72, 0.48, 0.62, 0.72, 0.33, 0.87, 0.19, 0.62, 0.45, 0.43, 0.83, 0.05,
0.91, 0.21, 0.75, 0.84, 0.19, 0.08, 0.09, 0.17, 0.81, 0.51, 0.72, 0.72,
0.42, 0.64, 0.6, 0.12, 0.07, 0.61, 0.73, 0.27, 0.17, 0.1, 0.45, 0.42,
0.49, 0.41, 0.76, 0.97, 0.53, 0.74, 0.43, 0.15, 0.93, 0.96, 0.67, 0.8,
0.57, 0.86, 0.78, 0.12, 0.34, 0.24, 0.06, 0.27, 0.91, 0.23, 0.62, 0.44,
0.73, 0.06, 0.88, 0.58, 0.5, 0.58, 0.99, 0.94, 0.35
]
]
}

18
benchmark/one_column/data_lar_100.json Normal file
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@@ -0,0 +1,18 @@
{
"input_data": [
[
9832.0, 9680.0, 3265.0, 9623.0, 1175.0, 4518.0, 8607.0, 126.0, 1208.0,
2166.0, 7854.0, 2806.0, 8745.0, 7209.0, 3717.0, 1468.0, 2073.0, 1947.0,
1275.0, 2438.0, 2864.0, 7951.0, 2086.0, 2361.0, 2833.0, 4246.0, 2418.0,
4413.0, 5482.0, 5231.0, 5314.0, 7580.0, 3801.0, 7274.0, 8171.0, 2790.0,
8590.0, 5837.0, 4078.0, 9147.0, 3698.0, 5530.0, 9338.0, 6268.0, 7889.0,
8007.0, 1349.0, 1351.0, 9746.0, 8635.0, 4812.0, 8485.0, 3173.0, 604.0,
286.0, 6016.0, 815.0, 9013.0, 1451.0, 7894.0, 6282.0, 5503.0, 430.0,
6646.0, 102.0, 2392.0, 9937.0, 9909.0, 8815.0, 8689.0, 5438.0, 4689.0,
8992.0, 7126.0, 3307.0, 1279.0, 8449.0, 1217.0, 8736.0, 946.0, 2417.0,
4278.0, 181.0, 6388.0, 3195.0, 4262.0, 3098.0, 288.0, 3002.0, 7274.0,
6790.0, 4375.0, 7741.0, 319.0, 3417.0, 3482.0, 122.0, 4318.0, 6529.0,
9245.0
]
]
}

0
benchmark/one_column/data22.json → benchmark/one_column/data_lar_1000.json
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0
benchmark/one_column/data12.json → benchmark/one_column/data_lar_300.json
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0
benchmark/one_column/data02.json → benchmark/one_column/data_lar_50.json
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73
benchmark/one_column/data_lar_600.json Normal file
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@@ -0,0 +1,73 @@
{
"input_data": [
[
6420.0, 1529.0, 6531.0, 2574.0, 534.0, 5508.0, 5078.0, 9915.0, 7511.0,
2704.0, 9278.0, 6063.0, 4817.0, 6998.0, 3345.0, 8104.0, 3893.0, 8529.0,
4363.0, 9007.0, 1472.0, 4370.0, 1799.0, 9993.0, 6538.0, 5007.0, 9787.0,
2615.0, 1558.0, 6070.0, 6899.0, 7506.0, 9675.0, 2082.0, 1580.0, 6976.0,
4747.0, 6089.0, 9492.0, 9749.0, 2865.0, 9833.0, 4704.0, 3838.0, 5459.0,
6064.0, 3504.0, 6002.0, 6470.0, 7024.0, 8286.0, 5813.0, 4328.0, 9249.0,
3071.0, 9373.0, 3518.0, 3808.0, 951.0, 1395.0, 9571.0, 9411.0, 2971.0,
6084.0, 5164.0, 3651.0, 2625.0, 6238.0, 5729.0, 8576.0, 1582.0, 4520.0,
1339.0, 8360.0, 283.0, 5995.0, 9178.0, 584.0, 7776.0, 2428.0, 7649.0,
4376.0, 9365.0, 102.0, 3814.0, 7522.0, 6055.0, 6257.0, 1516.0, 3324.0,
614.0, 8681.0, 4666.0, 6464.0, 599.0, 9172.0, 5744.0, 4245.0, 4343.0,
951.0, 5940.0, 2731.0, 5508.0, 9305.0, 7888.0, 1708.0, 2537.0, 338.0,
6422.0, 680.0, 859.0, 6585.0, 4668.0, 9132.0, 6233.0, 8689.0, 1659.0,
1968.0, 6468.0, 4931.0, 9133.0, 4746.0, 8530.0, 9745.0, 9706.0, 3971.0,
9515.0, 5398.0, 9075.0, 9553.0, 6100.0, 9845.0, 9615.0, 3408.0, 8188.0,
6951.0, 3287.0, 2845.0, 4037.0, 8571.0, 6046.0, 4393.0, 458.0, 2920.0,
5447.0, 6135.0, 4440.0, 4261.0, 4722.0, 1230.0, 4782.0, 3852.0, 3489.0,
4019.0, 1429.0, 4180.0, 6255.0, 9230.0, 4768.0, 7890.0, 728.0, 487.0,
9937.0, 3330.0, 5290.0, 9004.0, 764.0, 1455.0, 1595.0, 5805.0, 488.0,
7251.0, 866.0, 190.0, 4812.0, 6225.0, 2110.0, 7201.0, 4050.0, 6324.0,
3835.0, 9969.0, 2903.0, 4315.0, 4480.0, 4671.0, 1827.0, 3140.0, 2317.0,
4706.0, 4197.0, 4880.0, 4279.0, 8129.0, 658.0, 599.0, 5166.0, 973.0,
6941.0, 7261.0, 5696.0, 8450.0, 2591.0, 9976.0, 6613.0, 2265.0, 3641.0,
9917.0, 1948.0, 757.0, 3487.0, 2489.0, 2580.0, 9540.0, 1862.0, 157.0,
1250.0, 4989.0, 1858.0, 3736.0, 3956.0, 984.0, 3236.0, 8537.0, 3721.0,
8101.0, 190.0, 6649.0, 1242.0, 6782.0, 9241.0, 5356.0, 259.0, 9203.0,
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145.0, 6255.0, 1781.0, 3358.0, 7299.0, 6994.0, 734.0, 8220.0, 5338.0,
2456.0, 5581.0, 7336.0, 2655.0, 9863.0, 6000.0, 3962.0, 7442.0, 7373.0,
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}

15
benchmark/one_column/data_med_100.json Normal file
View File

@@ -0,0 +1,15 @@
{
"input_data": [
[
19.2, 54.1, 16.5, 24.8, 42.7, 18.9, 78.8, 54.4, 27.4, 76.2, 43.4, 20.9,
2.9, 30.4, 21.4, 2.0, 5.6, 33.5, 4.8, 4.7, 57.5, 23.5, 40.1, 83.1, 78.9,
95.1, 41.1, 59.0, 59.2, 91.1, 20.9, 67.6, 44.1, 91.3, 89.9, 85.7, 92.6,
67.1, 90.0, 29.5, 40.9, 96.8, 2.3, 57.9, 93.2, 83.9, 10.4, 75.1, 24.2,
22.9, 21.2, 26.9, 96.8, 89.0, 68.0, 16.1, 90.1, 1.7, 79.6, 98.5, 21.3,
79.5, 9.2, 97.9, 21.6, 4.2, 66.1, 53.8, 79.5, 60.6, 66.9, 39.5, 50.1,
66.1, 96.4, 80.5, 61.9, 44.4, 84.8, 64.8, 23.2, 7.1, 21.1, 90.5, 29.2,
1.4, 54.8, 9.8, 41.1, 45.2, 56.6, 48.2, 61.3, 62.9, 2.7, 33.2, 62.5, 40.9,
33.6, 50.1
]
]
}

0
benchmark/one_column/data21.json → benchmark/one_column/data_med_1000.json
View File

0
benchmark/one_column/data11.json → benchmark/one_column/data_med_300.json
View File

0
benchmark/one_column/data01.json → benchmark/one_column/data_med_50.json
View File

56
benchmark/one_column/data_med_600.json Normal file
View File

@@ -0,0 +1,56 @@
{
"input_data": [
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}

16
benchmark/one_column/data_sm_100.json Normal file
View File

@@ -0,0 +1,16 @@
{
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103
benchmark/one_column/data_sm_1000.json Normal file
View File

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5174.0, 5327.0, 5236.0, 4926.0, 5042.0, 5033.0, 5460.0, 4787.0, 4959.0,
5099.0, 4601.0, 4923.0, 5421.0, 4881.0, 5035.0, 4826.0, 5065.0, 5137.0,
4955.0, 5004.0, 4852.0, 4829.0, 5021.0, 5064.0, 4963.0, 5177.0, 4848.0,
4840.0, 4596.0, 5068.0, 4957.0, 5024.0, 5103.0, 4562.0, 5080.0, 4746.0,
4960.0, 4833.0, 4722.0, 4604.0, 4927.0, 5041.0, 5263.0, 4789.0, 5341.0,
5307.0, 5123.0, 5075.0, 4902.0, 5031.0, 5413.0, 5365.0, 5166.0, 5020.0,
4725.0, 5380.0, 5341.0, 5127.0, 5049.0, 5131.0, 5122.0, 4847.0, 5023.0,
5058.0, 5001.0, 4724.0, 4750.0, 5196.0, 5578.0, 5196.0, 5122.0, 4843.0,
5344.0, 4842.0, 5053.0, 5123.0, 5299.0, 5403.0, 5284.0, 5268.0, 5185.0,
4997.0, 4840.0, 5392.0, 4961.0, 4718.0, 5217.0, 5094.0, 5227.0, 5126.0,
5097.0, 4965.0, 4714.0, 4947.0, 4861.0, 5392.0, 5048.0, 4643.0, 4915.0,
5096.0, 4960.0, 5268.0, 4928.0, 4525.0, 4884.0, 4982.0, 5109.0, 4540.0,
4933.0, 5204.0, 4793.0, 5010.0, 5032.0, 5210.0, 4786.0, 5154.0, 5017.0,
5075.0, 5198.0, 5244.0, 5280.0, 5082.0, 5075.0, 5238.0, 4952.0, 5227.0,
4909.0, 4948.0, 5001.0, 5023.0, 5242.0, 5084.0, 4712.0, 4850.0, 5077.0,
5012.0, 5041.0, 5229.0, 5192.0, 5084.0, 4903.0, 4981.0, 5234.0, 5356.0,
4972.0, 5017.0, 4831.0, 4889.0, 5123.0, 4978.0, 5418.0, 5184.0, 4976.0,
5235.0, 5071.0, 4983.0, 4871.0, 5285.0, 5197.0, 4787.0, 5132.0, 5406.0,
5138.0, 5048.0, 4747.0, 5093.0, 5049.0, 4900.0, 5298.0, 4924.0, 4731.0,
5273.0, 4899.0, 5097.0, 4881.0, 4770.0, 4937.0, 5254.0, 4760.0, 4976.0,
5236.0, 5028.0, 4950.0, 4957.0, 5213.0, 4900.0, 5064.0, 4974.0, 5112.0,
4651.0, 4756.0, 5267.0, 5186.0, 4911.0, 4699.0
]
]
}

3
playground.py
View File

@@ -3,7 +3,8 @@
import numpy as np
# generate data for 1 col
array = np.round(np.random.uniform(100,10000, 1000), 0)
# array = np.floor(np.random.uniform(1,100, 50))
array = np.round(np.random.normal(5000, 200,1000), 0)
print(", ".join(map(str, array)))