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Merge pull request #12 from ZKStats/dev

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onefile format, benchmark (w/readme + optimized logrow)
This commit is contained in:
Kevin Mai-Husan Chia
2024-02-01 19:46:35 +08:00
committed by GitHub
parent adffa15f3d a4a552a74c
commit e3e10168b6
114 changed files with 18016 additions and 8611 deletions
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325
bench.py
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@@ -1,19 +1,32 @@
# For comparing among different datasets. Mostly similar to core.py
# For comparing among different datasets. Mostly similar to core.py
# but make it print less info to make us see bigger picture better
import sys
import importlib.util
from typing import Type
import torch
from torch import Tensor
import ezkl
import os
import numpy as np
import json
import time
import math
# Export model
def export_onnx(model, data_tensor_array, model_loc):
circuit = model()
# Try running `prepare()` if it exists
try:
circuit.prepare(data_tensor_array)
except AttributeError:
pass
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# print(device)
circuit.to(device)
# Flips the neural net into inference mode
@@ -39,13 +52,14 @@ def export_onnx(model, data_tensor_array, model_loc):
input_names = input_names, # the model's input names
output_names = ['output'], # the model's output names
dynamic_axes=dynamic_axes)
# ===================================================================================================
# ===================================================================================================
# ===================================================================================================
# ===================================================================================================
# mode is either "accuracy" or "resources"
def gen_settings(comb_data_path, onnx_filename, scale, mode, settings_filename):
# print("==== Generate & Calibrate Setting ====")
# sel_data = selected column from data that will be used for computation
def gen_settings(sel_data_path, onnx_filename, scale, mode, settings_filename):
# print("==== Generate & Calibrate Setting ====")
# Set input to be Poseidon Hash, and param of computation graph to be public
# Poseidon is not homomorphic additive, maybe consider Pedersens or Dory commitment.
gip_run_args = ezkl.PyRunArgs()
@@ -57,19 +71,71 @@ def gen_settings(comb_data_path, onnx_filename, scale, mode, settings_filename):
ezkl.gen_settings(onnx_filename, settings_filename, py_run_args=gip_run_args)
if scale =="default":
ezkl.calibrate_settings(
comb_data_path, onnx_filename, settings_filename, mode)
sel_data_path, onnx_filename, settings_filename, mode)
else:
ezkl.calibrate_settings(
comb_data_path, onnx_filename, settings_filename, mode, scales = scale)
sel_data_path, onnx_filename, settings_filename, mode, scales = scale)
assert os.path.exists(settings_filename)
assert os.path.exists(comb_data_path)
assert os.path.exists(sel_data_path)
assert os.path.exists(onnx_filename)
f_setting = open(settings_filename, "r")
# print("scale: ", scale)
# print("setting: ", f_setting.read())
# ===================================================================================================
# ===================================================================================================
# ===================================================================================================
# ===================================================================================================
# Here prover can concurrently call this since all params are public to get pk.
# Here dummy_sel_data_path is redundant, but here to use process_data
def verifier_define_calculation(dummy_data_path,col_array, dummy_sel_data_path, verifier_model, verifier_model_path):
dummy_data_tensor_array = process_data(dummy_data_path, col_array, dummy_sel_data_path)
# export onnx file
export_onnx(verifier_model, dummy_data_tensor_array, verifier_model_path)
# given data file (whole json table), create a dummy data file with randomized data
def create_dummy(data_path, dummy_data_path):
data = json.loads(open(data_path, "r").read())
# assume all columns have same number of rows
dummy_data ={}
for col in data:
# not use same value for every column to prevent something weird, like singular matrix
dummy_data[col] = np.round(np.random.uniform(1,30,len(data[col])),1).tolist()
json.dump(dummy_data, open(dummy_data_path, 'w'))
# ===================================================================================================
# ===================================================================================================
# New version
def process_data(data_path,col_array, sel_data_path) -> list[Tensor]:
data_tensor_array=[]
sel_data = []
data_onefile = json.loads(open(data_path, "r").read())
for col in col_array:
data = data_onefile[col]
data_tensor = torch.tensor(data, dtype = torch.float64)
data_tensor_array.append(torch.reshape(data_tensor, (1,-1,1)))
sel_data.append(data)
# Serialize data into file:
# sel_data comes from `data`
json.dump(dict(input_data = sel_data), open(sel_data_path, 'w' ))
return data_tensor_array
# we decide to not have sel_data_path as parameter since a bit redundant parameter.
def prover_gen_settings(data_path, col_array, sel_data_path, prover_model,prover_model_path, scale, mode, settings_path):
data_tensor_array = process_data(data_path,col_array, sel_data_path)
# export onnx file
export_onnx(prover_model, data_tensor_array, prover_model_path)
# gen + calibrate setting
gen_settings(sel_data_path, prover_model_path, scale, mode, settings_path)
# ===================================================================================================
# ===================================================================================================
# Here prover can concurrently call this since all params are public to get pk.
# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure
def verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path ):
# compile circuit
@@ -79,8 +145,8 @@ def verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_p
# srs path
res = ezkl.get_srs(settings_path)
# setupt vk, pk param for use..... prover can use same pk or can init their own!
# print("==== setting up ezkl ====")
# setup vk, pk param for use..... prover can use same pk or can init their own!
# print("==== setting up ezkl ====")
start_time = time.time()
res = ezkl.setup(
verifier_compiled_model_path,
@@ -88,51 +154,82 @@ def verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_p
pk_path)
end_time = time.time()
time_setup = end_time -start_time
# print(f"Time setup: {time_setup} seconds")
# print(f"Time setup: {time_setup} seconds")
assert res == True
assert os.path.isfile(vk_path)
assert os.path.isfile(pk_path)
assert os.path.isfile(settings_path)
# ===================================================================================================
# ===================================================================================================
# ===================================================================================================
# ===================================================================================================
# return time gen proof
def prover_gen_proof(prover_model_path, comb_data_path, witness_path, prover_compiled_model_path, settings_path, proof_path, pk_path):
res = ezkl.compile_circuit(prover_model_path, prover_compiled_model_path, settings_path)
assert res == True
# now generate the witness file
# print('==== Generating Witness ====')
witness = ezkl.gen_witness(comb_data_path, prover_compiled_model_path, witness_path)
assert os.path.isfile(witness_path)
# print(witness["outputs"])
settings = json.load(open(settings_path))
output_scale = settings['model_output_scales']
# print("witness boolean: ", ezkl.vecu64_to_float(witness['outputs'][0][0], output_scale[0]))
# for i in range(len(witness['outputs'][1])):
# print("witness result", i+1,":", ezkl.vecu64_to_float(witness['outputs'][1][i], output_scale[1]))
def prover_setup(
data_path,
col_array,
sel_data_path,
prover_model,
prover_model_path,
prover_compiled_model_path,
scale,
mode,
settings_path,
vk_path,
pk_path,
):
data_tensor_array = process_data(data_path, col_array, sel_data_path)
# GENERATE A PROOF
# print("==== Generating Proof ====")
start_time = time.time()
res = ezkl.prove(
witness_path,
prover_compiled_model_path,
pk_path,
proof_path,
"single",
)
# export onnx file
export_onnx(prover_model, data_tensor_array, prover_model_path)
# gen + calibrate setting
gen_settings(sel_data_path, prover_model_path, scale, mode, settings_path)
verifier_setup(prover_model_path, prover_compiled_model_path, settings_path, vk_path, pk_path)
# print("proof: " ,res)
end_time = time.time()
time_gen_prf = end_time -start_time
# print(f"Time gen prf: {time_gen_prf} seconds")
assert os.path.isfile(proof_path)
return time_gen_prf
# ===================================================================================================
# ===================================================================================================
def prover_gen_proof(
prover_model_path,
sel_data_path,
witness_path,
prover_compiled_model_path,
settings_path,
proof_path,
pk_path
):
# print("!@# compiled_model exists?", os.path.isfile(prover_compiled_model_path))
res = ezkl.compile_circuit(prover_model_path, prover_compiled_model_path, settings_path)
# print("!@# compiled_model exists?", os.path.isfile(prover_compiled_model_path))
assert res == True
# now generate the witness file
# print('==== Generating Witness ====')
witness = ezkl.gen_witness(sel_data_path, prover_compiled_model_path, witness_path)
assert os.path.isfile(witness_path)
# print(witness["outputs"])
settings = json.load(open(settings_path))
output_scale = settings['model_output_scales']
# print("witness boolean: ", ezkl.vecu64_to_float(witness['outputs'][0][0], output_scale[0]))
# for i in range(len(witness['outputs'][1])):
# print("witness result", i+1,":", ezkl.vecu64_to_float(witness['outputs'][1][i], output_scale[1]))
# GENERATE A PROOF
# print("==== Generating Proof ====")
start_time = time.time()
res = ezkl.prove(
witness_path,
prover_compiled_model_path,
pk_path,
proof_path,
"single",
)
# print("proof: " ,res)
end_time = time.time()
time_gen_prf = end_time -start_time
# print(f"Time gen prf: {time_gen_prf} seconds")
assert os.path.isfile(proof_path)
return time_gen_prf
# ===================================================================================================
# ===================================================================================================
# return result array
def verifier_verify(proof_path, settings_path, vk_path):
@@ -142,13 +239,13 @@ def verifier_verify(proof_path, settings_path, vk_path):
proof = json.load(open(proof_path))
num_inputs = len(settings['model_input_scales'])
# print("num_inputs: ", num_inputs)
# print("num_inputs: ", num_inputs)
proof["instances"][0][num_inputs] = ezkl.float_to_vecu64(1.0, output_scale[0])
json.dump(proof, open(proof_path, 'w'))
# print("prf instances: ", proof['instances'])
# print("prf instances: ", proof['instances'])
# print("proof boolean: ", ezkl.vecu64_to_float(proof['instances'][0][num_inputs], output_scale[0]))
# print("proof boolean: ", ezkl.vecu64_to_float(proof['instances'][0][num_inputs], output_scale[0]))
assert ezkl.vecu64_to_float(proof['instances'][0][num_inputs], output_scale[0]) == 1, "Prf not set to 1"
result = []
for i in range(num_inputs+1, len(proof['instances'][0])):
@@ -166,11 +263,12 @@ def verifier_verify(proof_path, settings_path, vk_path):
# print("verified")
return result
# ===================================================================================================
# ===================================================================================================
# ===================================================================================================
# ===================================================================================================
# just one dataset at a time.
def bench_one(data_path_array, model_func, gen_param_func, data_name, scale,mode):
def bench_one(data_path, col_array, model_func, gen_param_func, data_name, scale,logrow, mode):
os.makedirs(os.path.dirname('shared/'), exist_ok=True)
os.makedirs(os.path.dirname('prover/'), exist_ok=True)
verifier_model_path = os.path.join('shared/verifier.onnx')
@@ -181,41 +279,37 @@ def bench_one(data_path_array, model_func, gen_param_func, data_name, scale,mode
vk_path = os.path.join('shared/test.vk')
proof_path = os.path.join('shared/test.pf')
settings_path = os.path.join('shared/settings.json')
srs_path = os.path.join('shared/kzg.srs')
witness_path = os.path.join('prover/witness.json')
# this is private to prover since it contains actual data
comb_data_path = os.path.join('prover/comb_data.json')
sel_data_path = os.path.join('prover/sel_data.json')
# this is just dummy random value
sel_dummy_data_path = os.path.join('shared/sel_dummy_data.json')
dummy_data_path = os.path.join('shared/dummy_data.json')
print("===================================== ", data_name," =====================================")
# go through each dataset (we have 9 data sets)
data_tensor_array=[]
dummy_data_tensor_array = []
comb_data = []
for path in data_path_array:
data = np.array(json.loads(open(path, "r").read())["input_data"][0])
data_tensor_array.append(torch.reshape(torch.tensor(data), (1, len(data),1 )))
comb_data.append(data.tolist())
# 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' ))
create_dummy(data_path, dummy_data_path)
dummy_data_tensor_array = process_data(dummy_data_path, col_array, sel_dummy_data_path)
# verifier_define_calculation
export_onnx(model_func(gen_param_func(dummy_data_tensor_array)),dummy_data_tensor_array, verifier_model_path)
# prover_gen_settings
data_tensor_array = process_data(data_path,col_array, sel_data_path)
# export onnx file
export_onnx(model_func(gen_param_func(data_tensor_array)), data_tensor_array, prover_model_path)
# gen + calibrate setting
gen_settings(comb_data_path, prover_model_path, scale, mode, settings_path)
f_setting = open(settings_path, "r")
print("setting: ", f_setting.read())
gen_settings(sel_data_path, prover_model_path, scale, mode, settings_path)
f_setting = json.loads(open(settings_path, "r").read())
print("og setting: ", f_setting)
# f_setting['run_args']['logrows']=math.ceil(math.log(f_setting['num_rows'],2))
f_setting['run_args']['logrows']=logrow
json.dump(f_setting, open(settings_path, "w"), indent=2) # You can adjust the 'indent' parameter for formatting
print("logrow cal settings: ",json.loads(open(settings_path, "r").read()) )
verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )
gen_prf_time = prover_gen_proof(prover_model_path, comb_data_path, witness_path, prover_compiled_model_path, settings_path, proof_path, pk_path)
gen_prf_time = prover_gen_proof(prover_model_path, sel_data_path, witness_path, prover_compiled_model_path, settings_path, proof_path, pk_path)
result= verifier_verify(proof_path, settings_path, vk_path)
@@ -224,84 +318,5 @@ def bench_one(data_path_array, model_func, gen_param_func, data_name, scale,mode
print("gen prf time: ", gen_prf_time)
print("Theory result: ", gen_param_func(data_tensor_array)[0])
print("Our result: ", result)
return gen_prf_time
# ===================================================================================================
# ===================================================================================================
# to run bench of all datasets at the same time--> mostly 9 dataset
def bench_all(data_path_nested_array, model_func, gen_param_func, scale):
os.makedirs(os.path.dirname('shared/'), exist_ok=True)
os.makedirs(os.path.dirname('prover/'), exist_ok=True)
verifier_model_path = os.path.join('shared/verifier.onnx')
prover_model_path = os.path.join('prover/prover.onnx')
verifier_compiled_model_path = os.path.join('shared/verifier.compiled')
prover_compiled_model_path = os.path.join('prover/prover.compiled')
pk_path = os.path.join('shared/test.pk')
vk_path = os.path.join('shared/test.vk')
proof_path = os.path.join('shared/test.pf')
settings_path = os.path.join('shared/settings.json')
witness_path = os.path.join('prover/witness.json')
# this is private to prover since it contains actual data
comb_data_path = os.path.join('prover/comb_data.json')
for dataset_index in range(len(data_path_nested_array)):
data_path_array = data_path_nested_array[dataset_index]
match dataset_index:
case 0:
data_name = " data 00, ~50 small values "
case 1:
data_name = " data 01, ~50 medium values "
case 2:
data_name = " data 02, ~50 large values "
case 3:
data_name = " data 10, ~300 small values "
case 4:
data_name = " data 11, ~300 medium values "
case 5:
data_name = " data 12, ~300 large values "
case 6:
data_name = " data 20, ~1000 small values "
case 7:
data_name = " data 21, ~1000 medium values "
case 8:
data_name = " data 22, ~1000 large values "
print("Test:", data_name," ===============================================")
# go through each dataset (we have 9 data sets)
data_tensor_array=[]
dummy_data_tensor_array = []
comb_data = []
for path in data_path_array:
data = np.array(json.loads(open(path, "r").read())["input_data"][0])
data_tensor_array.append(torch.reshape(torch.tensor(data), (1, len(data),1 )))
comb_data.append(data.tolist())
# create dummy part, not need to save dummy data part
dummy_data = np.round(np.random.uniform(1,10,len(data)),1)
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' ))
# verifier_define_calculation
export_onnx(model_func(gen_param_func(dummy_data_tensor_array)),dummy_data_tensor_array, verifier_model_path)
# prover_gen_settings
# export onnx file
export_onnx(model_func(gen_param_func(data_tensor_array)), data_tensor_array, prover_model_path)
# gen + calibrate setting
gen_settings(comb_data_path, prover_model_path, [scale[dataset_index]], "resources", settings_path)
f_setting = open(settings_path, "r")
print("setting: ", f_setting.read())
verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )
gen_prf_time = prover_gen_proof(prover_model_path, comb_data_path, witness_path, prover_compiled_model_path, settings_path, proof_path, pk_path)
result= verifier_verify(proof_path, settings_path, vk_path)
# f_setting = open(settings_path, "r")
# print("setting: ", f_setting.read())
print("gen prf time: ", gen_prf_time)
print("Theory result: ", gen_param_func(data_tensor_array)[0])
print("Our result: ", result)
print("====================================================================")

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@@ -0,0 +1,51 @@
{
"small": [
0.277, 0.053, 0.892, 0.843, 0.801, 0.732, 0.672, 0.321, 0.812, 0.658, 0.281,
0.101, 0.496, 0.038, 0.798, 0.349, 0.829, 0.946, 0.783, 0.777, 0.172, 0.842,
0.157, 0.019, 0.919, 0.406, 0.375, 0.996, 0.697, 0.485, 0.604, 0.22, 0.931,
0.409, 0.956, 0.242, 0.158, 0.167, 0.296, 0.614, 0.524, 0.938, 0.583, 0.439,
0.652, 0.995, 0.508, 0.325, 0.183, 0.148, 0.798, 0.351, 0.905, 0.785, 0.041,
0.113, 0.774, 0.169, 0.89, 0.618, 0.065, 0.387, 0.409, 0.831, 0.904, 0.868,
0.131, 0.446, 0.236, 0.77, 0.813, 0.82, 0.817, 0.143, 0.167, 0.879, 0.488,
0.851, 0.249, 0.886, 0.519, 0.353, 0.928, 0.194, 0.76, 0.237, 0.644, 0.126,
0.075, 0.455, 0.502, 0.989, 0.294, 0.573, 0.959, 0.189, 0.602, 0.171, 0.999,
0.144
],
"medium": [
46.2, 40.4, 44.8, 48.1, 51.2, 91.9, 38.2, 36.3, 22.2, 11.5, 17.9, 20.2,
99.9, 75.2, 29.8, 19.4, 46.1, 94.8, 6.6, 94.5, 99.7, 1.6, 4.0, 86.7, 28.7,
63.0, 66.7, 2.5, 41.4, 35.6, 45.0, 13.7, 9.6, 16.6, 9.8, 20.3, 25.9, 71.9,
27.5, 30.9, 62.9, 18.6, 45.7, 2.4, 91.4, 16.2, 61.5, 41.4, 77.1, 53.2, 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
],
"large": [
7633.0, 5434.0, 2532.0, 9253.0, 8232.0, 3343.0, 710.0, 9010.0, 1885.0,
4667.0, 6712.0, 2149.0, 8767.0, 1516.0, 7410.0, 7141.0, 9505.0, 7395.0,
2358.0, 1084.0, 7183.0, 9974.0, 3204.0, 1291.0, 9701.0, 7469.0, 8615.0,
1623.0, 8490.0, 3453.0, 4139.0, 8970.0, 4348.0, 4839.0, 8594.0, 2432.0,
5574.0, 3449.0, 1164.0, 1495.0, 5496.0, 8075.0, 6810.0, 4911.0, 4130.0,
5878.0, 5255.0, 1586.0, 4697.0, 8595.0, 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
],
"large_std": [
4811.0, 4777.0, 4876.0, 5212.0, 5189.0, 4977.0, 5167.0, 4949.0, 4773.0,
5115.0, 5156.0, 5120.0, 5000.0, 4810.0, 4660.0, 5296.0, 5062.0, 4616.0,
5226.0, 4787.0, 4729.0, 4860.0, 5094.0, 4813.0, 5543.0, 4527.0, 4845.0,
4915.0, 5366.0, 5023.0, 5270.0, 4834.0, 5005.0, 5042.0, 5242.0, 4719.0,
4834.0, 5548.0, 4942.0, 5303.0, 4624.0, 5145.0, 5032.0, 4726.0, 4895.0,
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}

403
benchmark/for_testing_only/data_1000.json Normal file
View File

@@ -0,0 +1,403 @@
{
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"medium": [
46.2, 40.4, 44.8, 48.1, 51.2, 91.9, 38.2, 36.3, 22.2, 11.5, 17.9, 20.2,
99.9, 75.2, 29.8, 19.4, 46.1, 94.8, 6.6, 94.5, 99.7, 1.6, 4.0, 86.7, 28.7,
63.0, 66.7, 2.5, 41.4, 35.6, 45.0, 13.7, 9.6, 16.6, 9.8, 20.3, 25.9, 71.9,
27.5, 30.9, 62.9, 18.6, 45.7, 2.4, 91.4, 16.2, 61.5, 41.4, 77.1, 53.2, 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, 80.3, 96.2, 62.3,
17.2, 23.0, 98.0, 19.1, 8.1, 36.2, 7.5, 55.9, 1.2, 56.8, 85.1, 18.9, 23.0,
13.5, 64.3, 9.1, 14.1, 14.1, 23.1, 73.2, 86.6, 39.1, 45.5, 85.0, 79.0, 15.8,
5.2, 81.5, 34.3, 24.3, 14.2, 84.6, 33.7, 86.3, 83.3, 62.8, 72.7, 14.7, 36.8,
92.5, 4.7, 30.0, 59.4, 57.6, 37.4, 22.0, 20.9, 61.6, 26.8, 47.1, 63.6, 6.0,
96.6, 61.2, 80.2, 59.3, 23.1, 29.3, 46.3, 89.2, 77.6, 83.2, 87.2, 63.2,
81.8, 55.0, 59.7, 57.8, 43.4, 92.4, 66.9, 82.1, 51.0, 22.1, 29.9, 41.0,
85.2, 61.5, 14.6, 48.0, 52.7, 31.4, 83.9, 35.5, 77.3, 35.8, 32.6, 22.2,
19.3, 49.1, 70.9, 43.9, 88.8, 56.3, 41.8, 90.3, 20.4, 80.4, 36.4, 91.5,
69.6, 75.3, 92.4, 84.8, 17.7, 2.3, 41.3, 91.3, 68.6, 73.3, 62.5, 60.5, 73.5,
70.7, 77.5, 76.8, 98.1, 40.9, 66.3, 8.6, 48.9, 75.4, 14.7, 35.9, 89.6, 15.1,
45.0, 77.6, 30.5, 76.1, 46.9, 34.3, 65.1, 43.9, 91.6, 88.8, 8.9, 42.9, 11.8,
32.1, 20.1, 48.9, 79.7, 15.3, 45.4, 80.1, 73.1, 76.5, 52.4, 9.6, 41.9, 52.7,
55.1, 30.9, 83.7, 46.7, 39.3, 40.5, 52.4, 19.2, 25.8, 52.7, 81.0, 38.0,
54.5, 15.3, 64.3, 88.3, 49.8, 90.5, 90.4, 79.7, 87.3, 32.3, 11.9, 5.7, 33.6,
75.1, 65.9, 29.1, 39.4, 87.5, 3.3, 66.3, 79.0, 97.9, 69.6, 22.0, 62.8, 97.1,
90.4, 39.5, 11.7, 30.3, 18.9, 34.6, 6.6, 5.4, 16.5, 72.3, 10.3, 4.5, 26.3,
90.7, 23.2, 46.4, 82.0, 36.8, 71.9, 99.3, 69.4, 83.5, 6.2, 4.8, 4.4, 1.1,
28.8, 34.7, 8.5, 64.7, 84.0, 60.9, 76.0, 98.9, 97.0, 97.7, 4.5, 5.9, 42.6,
56.7, 18.3, 12.3, 98.0, 41.7, 1.5, 12.6, 6.8, 64.9, 94.2, 50.2, 20.8, 32.4,
94.2, 2.3, 97.6, 4.2, 42.3, 58.9, 34.6, 71.2, 98.2, 71.9, 85.4, 1.4, 16.7,
53.6, 97.1, 50.5, 56.4, 38.4, 46.3, 77.4, 89.5, 19.4, 12.9, 82.8, 43.8,
81.2, 16.7, 32.9, 90.5, 14.3, 49.8, 9.6, 75.8, 62.3, 55.1, 66.4, 11.8, 79.9,
81.7, 50.3, 52.4, 58.2, 33.8, 9.0, 45.8, 56.7, 68.4, 29.5, 26.9, 32.5, 5.8,
10.5, 41.2, 64.3, 30.6, 47.3, 5.3, 76.7, 46.8, 86.2, 45.6, 35.7, 43.8, 12.1,
92.5, 59.4, 13.1, 31.6, 90.0, 61.0, 80.8, 56.3, 45.7, 98.0, 38.3, 95.2,
53.7, 99.0, 37.9, 97.5, 36.1, 32.0, 10.3, 33.0, 56.7, 95.1, 60.9, 19.7, 8.6,
33.6, 25.5, 80.5, 6.5, 6.2, 96.2, 91.3, 60.4, 27.6, 55.4, 41.6, 86.1, 64.4,
18.5, 88.0, 54.4, 5.1, 41.1, 81.7, 18.7, 82.5, 73.9, 83.9, 50.1, 8.6, 25.0,
33.9, 72.2, 79.4, 28.8, 46.6, 14.2, 61.3, 83.0, 8.6, 26.7, 68.2, 7.0, 75.7,
74.7, 25.6, 28.9, 67.8, 95.7, 32.5, 49.8, 86.1, 68.1, 66.1, 53.9, 49.2,
79.4, 96.3, 16.1, 96.9, 44.3, 36.3, 56.9, 59.7, 7.1, 91.7, 43.9, 53.3, 27.5,
4.2, 21.1, 48.2, 91.6, 92.4, 26.1, 49.2, 63.3, 56.0, 32.3, 3.3, 22.3, 64.5,
85.0, 36.3, 12.6, 22.6, 93.6, 77.4, 70.8, 11.0, 42.7, 57.3, 31.6, 41.4,
71.9, 86.8, 13.7, 30.0, 2.5, 57.0, 79.2, 36.2, 69.7, 18.0, 89.1, 32.0, 53.5,
95.0, 96.0, 14.1, 52.2, 96.1, 60.9, 54.9, 80.9, 48.9, 91.7, 71.9, 34.3,
35.3, 90.8, 48.2, 9.7, 33.2, 84.4, 33.9, 2.4, 75.2, 7.4, 6.2, 92.4, 73.2,
6.9, 45.4, 64.3, 29.3, 15.5, 48.9, 87.8, 81.9, 20.4, 70.4, 73.0, 72.4, 22.4,
26.9, 73.6, 44.8, 18.6, 14.7, 84.7, 80.4, 83.3, 69.2, 82.2, 9.0, 12.7, 86.0,
37.7, 28.1, 42.3, 35.8, 54.5, 51.0, 96.3, 24.3, 28.5, 84.4, 1.9, 37.3, 95.9,
75.1, 33.1, 80.6, 56.6, 84.1, 79.8, 42.1, 46.2, 15.7, 17.1, 90.9, 23.0,
90.9, 18.0, 38.6, 82.4, 4.0, 9.4, 68.6, 96.8, 84.7, 17.1, 53.4, 45.7, 59.8,
26.8, 52.7, 23.6, 92.0, 68.1, 70.3, 4.0, 89.2, 36.0, 85.5, 56.5, 3.6, 14.1,
69.4, 35.7, 39.9, 43.9, 98.2, 54.3, 1.8, 26.4, 95.9, 78.6, 64.3, 8.9, 6.5,
55.5, 67.6, 74.8, 78.0, 45.3, 14.7, 54.0, 32.8, 85.8, 53.3, 4.8, 92.7, 36.6,
54.5, 72.7, 74.7, 98.8, 1.6, 46.6, 9.4, 12.2, 98.7, 44.4, 84.9, 7.2, 11.5,
11.0, 35.1, 18.5, 40.4, 24.8, 1.2, 81.8, 60.3, 21.5, 87.1, 23.6, 88.2, 20.2,
2.5, 67.6, 17.2, 28.1, 56.2, 61.8, 94.6, 61.0, 46.8, 20.2, 65.5, 7.2, 20.9,
40.5, 39.8, 50.2, 31.1, 16.1, 5.6, 11.4, 25.9, 97.3, 19.9, 79.2, 86.0, 86.1,
10.1, 74.5, 76.8, 46.3, 80.6, 6.6, 23.6, 43.3, 52.0, 19.7, 59.6, 9.7, 18.5,
83.6, 90.6, 32.1, 10.4, 8.3, 88.0, 67.4, 52.8, 56.7, 80.1, 93.3, 75.5, 79.0,
96.7, 53.4, 69.2, 83.6, 99.7, 4.0, 79.8, 98.8, 19.8, 91.6, 13.3, 19.4, 96.2,
97.5, 78.9, 58.6, 20.5, 22.6, 93.0, 23.9, 57.6, 25.9, 75.8, 54.1, 50.3,
89.7, 88.5, 72.2, 2.3, 6.7, 83.8, 15.2, 83.3, 51.4, 86.9, 23.5, 39.6, 24.6,
79.1, 97.5, 31.5, 91.6, 7.8, 91.4, 69.3, 11.2, 99.9, 34.2, 35.1, 6.5, 35.4,
67.5, 52.9, 100.0, 65.2, 68.1, 18.9, 16.3, 45.7, 22.5, 15.2, 21.7, 71.6,
97.2, 53.0, 85.5, 75.9, 90.6, 21.0, 94.6, 31.7, 18.4, 67.1, 39.6, 4.2, 52.6,
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93.2, 99.3, 33.4, 82.0, 35.5, 55.8, 35.0, 91.2, 41.6, 48.7, 61.7, 58.9,
69.8, 19.7, 36.8, 12.0, 72.5, 88.0, 33.1, 86.2, 2.9, 66.4, 6.2, 8.5, 68.4,
31.3, 68.3, 15.6, 45.5, 8.3, 54.7, 85.2, 6.4, 40.4, 87.3, 19.2, 95.3, 60.5,
26.2, 38.4, 85.0, 53.2, 92.0, 72.5, 52.3, 73.0, 82.8, 97.1, 18.5, 84.1,
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25.2, 39.0, 10.6, 58.5, 48.2, 41.6, 86.1, 83.7, 51.6, 62.3, 54.6, 83.5,
50.8, 61.4, 3.3, 48.5, 15.1, 46.9, 61.9, 58.7, 14.1, 5.2, 15.1, 10.2, 19.8,
43.9, 45.5, 44.0, 24.6, 92.5, 30.7, 63.8, 27.3, 67.3, 8.5, 97.3, 7.7, 37.5,
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129
benchmark/for_testing_only/data_300.json Normal file
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@@ -0,0 +1,129 @@
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31
benchmark/for_testing_only/data_50.json Normal file
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245
benchmark/for_testing_only/data_600.json Normal file
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benchmark/one_column/data_med_100.json
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89
benchmark/one_column/data_med_1000.json
View File

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31
benchmark/one_column/data_med_300.json
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10
benchmark/one_column/data_med_50.json
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56
benchmark/one_column/data_med_600.json
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16
benchmark/one_column/data_sm_100.json
View File

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103
benchmark/one_column/data_sm_1000.json
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35
benchmark/one_column/data_sm_300.json
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11
benchmark/one_column/data_sm_50.json
View File

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64
benchmark/one_column/data_sm_600.json
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benchmark/one_column/data_std_lar_100.json
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}

12
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{
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73
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{
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}

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This benchmark folder consists of 2 folders
- for_testing_only. No need to look at it. It is just a playground for me to test adjust parameters, no systematic testing yet.
- result: relevant information about the benchmark of some example functions.
From now on, will only talk about 'result' folder
Here we test data with 3 ranges of values as follows.
- small: value 0.1 - 1, with precision at 0.001
- medium: value 1 - 100, with precision at 0.1
- large: value 100 - 1000, with precision at 1
And we test these values across, a dataset of 50, 100, 300, 600, and 1000 numbers
Now, how do we optimize the setting for each computation task. Looking at the setting file that ezkl generates, we can adjust 'scale', 'logrow', and 'mode' in the detail as follows.
- 'mode' can be either 'resources' or 'accuracy'. For practicality, we will always set it to 'resources' to optimize for the circuit size, and then adjust other parameters to address the accuracy issue instead.
- 'scale' is for representing float values either on inputs/outputs or the intermediate results during the computation graph that our zkstats want to compute. For example, if the lowest float number in those intermediates is around 0.001, the scale can be around 10, so that 0.001 \* 2^10 becomes integer.
**We don't recommend to set it to default since it's mostly overkill.** Remember, the larger the scale is, the bigger size the circuit is. But sometimes the intermediate value in our computation requires that large value for example, if we need to calculate torch.log(), the floating point is very important, hence large scale.
- 'logrow' can be chosen arbitrarily between min(log_2(number of rows), log_2(lookup range)) to max(log_2(number of rows), log_2(lookup range)). The trade off is that if we choose lower logrow value , we will face with more "columns for non-linearity table".
- **For now, based on our experiment, the best value to select logrow is to select the lowest logrow in the range such that ceiling(lookup_range/2^logrow) <=5**

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{
"small": [
0.277, 0.053, 0.892, 0.843, 0.801, 0.732, 0.672, 0.321, 0.812, 0.658, 0.281,
0.101, 0.496, 0.038, 0.798, 0.349, 0.829, 0.946, 0.783, 0.777, 0.172, 0.842,
0.157, 0.019, 0.919, 0.406, 0.375, 0.996, 0.697, 0.485, 0.604, 0.22, 0.931,
0.409, 0.956, 0.242, 0.158, 0.167, 0.296, 0.614, 0.524, 0.938, 0.583, 0.439,
0.652, 0.995, 0.508, 0.325, 0.183, 0.148, 0.798, 0.351, 0.905, 0.785, 0.041,
0.113, 0.774, 0.169, 0.89, 0.618, 0.065, 0.387, 0.409, 0.831, 0.904, 0.868,
0.131, 0.446, 0.236, 0.77, 0.813, 0.82, 0.817, 0.143, 0.167, 0.879, 0.488,
0.851, 0.249, 0.886, 0.519, 0.353, 0.928, 0.194, 0.76, 0.237, 0.644, 0.126,
0.075, 0.455, 0.502, 0.989, 0.294, 0.573, 0.959, 0.189, 0.602, 0.171, 0.999,
0.144
],
"medium": [
52.3, 42.6, 88.3, 88.6, 24.8, 91.7, 65.9, 73.3, 90.4, 70.3, 50.1, 77.1,
59.8, 88.1, 33.1, 96.8, 5.9, 10.7, 93.9, 60.8, 14.5, 75.0, 36.4, 98.0, 41.7,
63.1, 68.8, 94.1, 67.3, 92.3, 53.6, 36.2, 6.9, 77.0, 65.4, 27.5, 77.9, 93.8,
39.7, 9.4, 64.0, 58.6, 83.2, 95.8, 24.0, 68.1, 5.7, 1.3, 41.7, 88.7, 26.3,
90.4, 72.3, 36.4, 80.2, 11.5, 44.8, 66.5, 39.6, 56.2, 97.1, 56.3, 32.0,
41.1, 71.0, 40.3, 61.1, 34.8, 81.5, 32.5, 71.6, 40.3, 3.2, 56.0, 14.2, 87.8,
99.5, 4.0, 3.1, 31.7, 83.9, 55.1, 61.8, 34.0, 96.9, 93.5, 45.5, 38.6, 95.2,
6.8, 95.3, 28.8, 30.7, 4.9, 48.5, 23.3, 14.1, 11.4, 56.5, 52.4
],
"large": [
627.0, 470.0, 770.0, 649.0, 455.0, 912.0, 146.0, 364.0, 664.0, 355.0, 166.0,
278.0, 200.0, 769.0, 820.0, 969.0, 135.0, 884.0, 792.0, 144.0, 919.0, 133.0,
731.0, 442.0, 626.0, 359.0, 844.0, 180.0, 874.0, 285.0, 608.0, 653.0, 789.0,
638.0, 795.0, 813.0, 272.0, 378.0, 260.0, 176.0, 882.0, 689.0, 777.0, 641.0,
167.0, 249.0, 906.0, 776.0, 662.0, 933.0, 810.0, 723.0, 905.0, 966.0, 923.0,
540.0, 649.0, 105.0, 983.0, 368.0, 945.0, 467.0, 118.0, 320.0, 762.0, 469.0,
899.0, 442.0, 358.0, 160.0, 147.0, 251.0, 301.0, 718.0, 823.0, 816.0, 158.0,
202.0, 824.0, 105.0, 268.0, 363.0, 542.0, 650.0, 665.0, 664.0, 206.0, 643.0,
746.0, 604.0, 450.0, 693.0, 847.0, 393.0, 138.0, 566.0, 554.0, 796.0, 701.0,
862.0
]
}

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{
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0.075, 0.455, 0.502, 0.989, 0.294, 0.573, 0.959, 0.189, 0.602, 0.171, 0.999,
0.144, 0.417, 0.637, 0.328, 0.15, 0.384, 0.523, 0.828, 0.443, 0.689, 0.217,
0.488, 0.791, 0.57, 0.392, 0.439, 0.353, 0.414, 0.322, 0.897, 0.463, 0.246,
0.084, 0.711, 0.264, 0.636, 0.3, 0.485, 0.081, 0.475, 0.476, 0.756, 0.176,
0.185, 0.662, 0.579, 0.348, 0.46, 0.207, 0.371, 0.414, 0.315, 0.878, 0.191,
0.008, 0.114, 0.032, 0.312, 0.796, 0.284, 0.238, 0.064, 0.186, 0.966, 0.422,
0.728, 0.551, 0.537, 0.848, 0.683, 0.062, 0.434, 0.061, 0.895, 0.67, 0.392,
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0.867, 0.933, 0.897, 0.14, 0.745, 0.873, 0.961, 0.046, 0.842, 0.543, 0.758,
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0.002, 0.307, 0.557, 0.565, 0.782, 0.68, 0.889, 0.853, 0.145, 0.986, 0.615,
0.15, 0.709, 0.04, 0.263, 0.947, 0.758, 0.034, 0.526, 0.338, 0.181, 0.848,
0.218, 0.564, 0.382, 0.411, 0.019, 0.914, 0.243, 0.853, 0.82, 0.677, 0.978,
0.479, 0.376, 0.385, 0.947, 0.695, 0.31, 0.174, 0.923, 0.425, 0.577, 0.886,
0.839, 0.171, 0.675, 0.003, 0.342, 0.084, 0.694, 0.024, 0.657, 0.273, 0.797,
0.835, 0.12, 0.69, 0.485, 0.43, 0.152, 0.438, 0.47, 0.53, 0.587, 0.834,
0.228, 0.801, 0.882, 0.359, 0.222, 0.777, 0.709, 0.951, 0.145, 0.896, 0.585,
0.932, 0.143, 0.381, 0.812, 0.051, 0.662, 0.282, 0.873, 0.928, 0.5, 0.204,
0.223, 0.498, 0.018, 0.781, 0.159, 0.267, 0.426, 0.784, 0.794, 0.338, 0.672,
0.9, 0.869, 0.123, 0.157, 0.92, 0.31, 0.744, 0.954, 0.69, 0.741, 0.276,
0.68, 0.021, 0.447, 0.69, 0.803, 0.147, 0.07, 0.247, 0.392, 0.842, 0.809,
0.431, 0.013, 0.064, 0.082, 0.591, 0.063, 0.507, 0.417, 0.977, 0.799, 0.117,
0.2, 0.4, 0.266, 0.66, 0.544, 0.925, 0.775, 0.609, 0.961, 0.471, 0.823,
0.062, 0.319, 0.295, 0.104, 0.778, 0.016, 0.789, 0.171, 0.513, 0.942, 0.426,
0.78, 0.115, 0.409, 0.379, 0.026, 0.672, 0.105, 0.39, 0.807, 0.86, 0.778,
0.97, 0.573, 0.478, 0.309, 0.355, 0.645, 0.329, 0.466, 0.433, 0.734, 0.938,
0.039, 0.029, 0.037, 0.539, 0.894, 0.818, 0.632, 0.578, 0.215, 0.526, 0.276,
0.979, 0.298, 0.225, 0.488, 0.064, 0.154, 0.647, 0.657, 0.593, 0.452, 0.524,
0.918, 0.282, 0.045, 0.25, 0.659, 0.374, 0.455, 0.302, 0.396, 0.485, 0.418,
0.525, 0.122, 0.844, 0.681, 0.903, 0.16, 0.262, 0.45, 0.894, 0.702, 0.555,
0.513, 0.901, 0.854, 0.627, 0.488, 0.243, 0.68, 0.05, 0.055, 0.289, 0.523,
0.072, 0.21, 0.118, 0.179, 0.391, 0.473, 0.489, 0.172, 0.484, 0.07, 0.767,
0.505, 0.07, 0.677, 0.622, 0.973, 0.818, 0.409, 0.3, 0.949, 0.728, 0.788,
0.69, 0.009, 0.873, 0.983, 0.172, 0.64, 0.154, 0.513, 0.412, 0.615, 0.876,
0.66, 0.439, 0.26, 0.119, 0.168, 0.866, 0.331, 0.065, 0.771, 0.652, 0.06,
0.333, 0.287, 0.322, 0.928, 0.004, 0.666, 0.317, 0.063, 0.434, 0.655, 0.361,
0.903, 0.467, 0.008, 0.407, 0.755, 0.83, 0.204, 0.803, 0.093, 0.228, 0.118,
0.167, 0.809, 0.221, 0.936, 0.156, 0.529, 0.622, 0.919, 0.166, 0.29, 0.379,
0.654, 0.54, 0.82, 0.154, 0.168, 0.763, 0.691, 0.764, 0.702, 0.396, 0.089,
0.927, 0.432, 0.731, 0.35, 0.724, 0.542, 0.925, 0.88, 0.521, 0.534, 0.851,
0.839, 0.115, 0.192, 0.283, 0.267, 0.951, 0.023, 0.622, 0.237, 0.492, 0.441,
0.144, 0.165, 0.155, 0.272, 0.867, 0.057, 0.486, 0.481, 0.257, 0.755, 0.613,
0.255, 0.468, 0.21, 0.584, 0.166, 0.481, 0.244, 0.112, 0.847, 0.657, 0.073,
0.792, 0.349, 0.786, 0.101, 0.163, 0.416, 0.695, 0.5, 0.192, 0.19, 0.485,
0.017, 0.272, 0.474, 0.666, 0.025, 0.421, 0.612, 0.685, 0.821, 0.971, 0.078,
0.147, 0.098, 0.976, 0.592, 0.387, 0.968, 0.613, 0.379, 0.783, 0.973, 0.055,
0.514, 0.209, 0.043, 0.955, 0.93, 0.114, 0.107, 0.594, 0.931, 0.632, 0.329,
0.019, 0.498, 0.196, 0.759, 0.825, 0.012, 0.312, 0.811, 0.805, 0.032, 0.603,
0.358, 0.972, 0.038, 0.905, 0.205, 0.499, 0.925, 0.112, 0.351, 0.862, 0.422,
0.233, 0.446, 0.204, 0.75, 0.124, 0.598, 0.046, 0.265, 0.075, 0.045, 0.14,
0.235, 0.271, 0.213, 0.323, 0.644, 0.624, 0.948, 0.738, 0.599, 0.182, 0.108,
0.883, 0.065, 0.219, 0.052, 0.655, 0.945, 0.299, 0.954, 0.481, 0.781, 0.357,
0.442, 0.238, 0.035, 0.795, 0.398, 0.158, 0.394, 0.292, 0.893, 0.813, 0.33,
0.215, 0.807, 0.08, 0.444, 0.715, 0.767, 0.471, 0.804, 0.636, 0.974, 0.222,
0.694, 0.384, 0.251, 0.275, 0.041, 0.905, 0.564, 0.394, 0.892, 0.143, 0.955,
0.199, 0.456, 0.179, 0.76, 0.554, 0.528, 0.025, 0.291, 0.947, 0.345, 0.953,
0.794, 0.764, 0.814, 0.606, 0.671, 0.774, 0.056, 0.299, 0.705, 0.934, 0.844,
0.605, 0.404, 0.193, 0.388, 0.307, 0.921, 0.424, 0.118, 0.005, 0.146, 0.528,
0.876, 0.973, 0.48, 0.525, 0.096, 0.141, 0.048, 0.064, 0.756, 0.109, 0.996,
0.548, 0.603, 0.385, 0.671, 0.649, 0.194, 0.061, 0.55, 0.298, 0.77, 0.285,
0.266, 0.959, 0.944, 0.29, 0.253, 0.26, 0.592, 0.645, 0.708, 0.464, 0.976,
0.888, 0.976, 0.655, 0.009, 0.988, 0.105, 0.449, 0.273, 0.008, 0.515, 0.209,
0.208, 0.585, 0.582, 0.409, 0.923, 0.648, 0.37, 0.426, 0.589, 0.517, 0.227,
0.18, 0.29, 0.01, 0.142, 0.485, 0.529, 0.047, 0.356, 0.022, 0.9, 0.406,
0.221, 0.805, 0.835, 0.962, 0.272, 0.025, 0.221, 0.769, 0.333, 0.214, 0.313,
0.81, 0.142, 0.994, 0.512, 0.505, 0.045, 0.306, 0.181, 0.942, 0.928, 0.257,
0.304, 0.445, 0.217, 0.234, 0.965, 0.714, 0.67, 0.508, 0.474, 0.085, 0.852,
0.609, 0.835, 0.633, 0.818, 0.086, 0.681, 0.594, 0.526, 0.811, 0.52, 0.889,
0.492, 0.243, 0.147, 0.694, 0.826, 0.92, 0.618, 0.28, 0.924, 0.355, 0.587,
0.613, 0.901, 0.412, 0.538, 0.991, 0.81, 0.449, 0.974, 0.123, 0.623, 0.258,
0.181, 0.28, 0.354, 0.845, 0.01, 0.508, 0.53, 0.908, 0.064, 0.588, 0.107,
0.301, 0.939, 0.548, 0.685, 0.321, 0.049, 0.049, 0.404, 0.54, 0.181, 0.553,
0.314, 0.355, 0.423, 0.297, 0.269, 0.409, 0.115, 0.647, 0.45, 0.241, 0.41,
0.689, 0.644, 0.165, 0.03, 0.889, 0.456, 0.801, 0.736, 0.857, 0.297
],
"medium": [
52.3, 42.6, 88.3, 88.6, 24.8, 91.7, 65.9, 73.3, 90.4, 70.3, 50.1, 77.1,
59.8, 88.1, 33.1, 96.8, 5.9, 10.7, 93.9, 60.8, 14.5, 75.0, 36.4, 98.0, 41.7,
63.1, 68.8, 94.1, 67.3, 92.3, 53.6, 36.2, 6.9, 77.0, 65.4, 27.5, 77.9, 93.8,
39.7, 9.4, 64.0, 58.6, 83.2, 95.8, 24.0, 68.1, 5.7, 1.3, 41.7, 88.7, 26.3,
90.4, 72.3, 36.4, 80.2, 11.5, 44.8, 66.5, 39.6, 56.2, 97.1, 56.3, 32.0,
41.1, 71.0, 40.3, 61.1, 34.8, 81.5, 32.5, 71.6, 40.3, 3.2, 56.0, 14.2, 87.8,
99.5, 4.0, 3.1, 31.7, 83.9, 55.1, 61.8, 34.0, 96.9, 93.5, 45.5, 38.6, 95.2,
6.8, 95.3, 28.8, 30.7, 4.9, 48.5, 23.3, 14.1, 11.4, 56.5, 52.4, 67.8, 69.7,
55.7, 73.4, 68.9, 51.0, 24.3, 54.1, 28.3, 29.8, 99.8, 27.2, 97.9, 65.3,
27.4, 29.9, 46.3, 72.4, 30.3, 61.4, 92.7, 59.7, 57.9, 35.0, 44.1, 69.2,
87.2, 62.4, 92.9, 45.3, 47.0, 38.7, 23.6, 62.4, 22.0, 9.3, 35.0, 98.2, 14.7,
91.8, 83.2, 80.9, 20.7, 54.0, 12.5, 33.8, 56.0, 75.0, 79.1, 48.2, 85.2,
33.8, 18.0, 39.8, 34.7, 18.8, 1.9, 51.5, 99.3, 51.6, 52.0, 84.5, 78.0, 3.6,
51.4, 63.4, 78.2, 96.4, 4.3, 66.0, 28.9, 23.3, 12.0, 48.3, 95.0, 56.6, 58.1,
19.5, 93.5, 15.1, 39.0, 83.9, 51.4, 92.4, 75.3, 86.6, 42.3, 56.3, 47.3,
54.5, 59.5, 85.9, 19.8, 21.0, 17.8, 35.7, 88.4, 80.4, 52.9, 44.7, 86.0,
64.2, 60.0, 70.2, 64.1, 20.3, 47.5, 35.4, 71.9, 35.9, 54.4, 21.6, 27.3,
11.1, 75.9, 93.7, 33.6, 80.5, 13.4, 61.1, 51.8, 23.9, 26.9, 23.8, 5.9, 37.6,
37.2, 49.6, 89.4, 76.0, 89.2, 38.3, 74.6, 72.5, 86.5, 77.8, 61.0, 2.7, 19.6,
57.9, 26.5, 58.3, 88.8, 47.7, 12.0, 37.1, 17.7, 32.8, 54.1, 71.0, 8.9, 25.0,
42.4, 60.4, 33.6, 52.8, 52.0, 28.9, 63.4, 91.3, 5.3, 99.3, 76.9, 27.6, 46.2,
85.0, 77.2, 45.5, 46.0, 99.1, 41.9, 31.1, 57.8, 77.2, 2.4, 17.9, 35.9, 28.9,
34.4, 64.6, 12.7, 10.4, 61.8, 95.7, 12.1, 74.9, 51.7, 37.1, 89.9, 86.8,
62.6, 28.8, 54.4, 83.5, 57.5, 10.2, 89.3, 74.9, 51.9, 72.1, 24.9, 59.9,
37.6, 97.8, 63.2, 30.2, 23.8, 95.0, 71.8, 38.1, 65.0, 85.9, 70.9, 73.3, 1.5,
15.0, 83.4, 23.6, 71.5, 95.6, 60.6, 18.9, 69.9, 58.2, 43.3, 40.9, 4.3, 79.6,
30.9, 62.1, 72.5, 55.1, 15.3, 6.0, 41.7, 55.7, 31.8, 45.2, 60.7, 58.7, 44.4,
27.7, 73.0, 8.6, 73.0, 35.5, 86.1, 2.5, 7.1, 92.6, 76.3, 12.6, 80.8, 29.8,
20.1, 96.4, 81.7, 67.3, 28.9, 27.4, 67.3, 95.8, 67.8, 8.9, 25.5, 97.7, 29.6,
39.4, 51.4, 5.8, 16.9, 37.1, 88.8, 88.7, 5.8, 34.4, 36.2, 80.1, 2.1, 77.9,
87.6, 42.5, 64.6, 80.6, 25.6, 71.9, 72.3, 3.6, 80.1, 54.4, 58.9, 3.4, 39.1,
84.6, 51.5, 60.1, 81.3, 83.8, 60.9, 64.7, 41.5, 46.8, 26.4, 98.4, 66.9,
88.5, 24.1, 45.1, 90.6, 96.0, 65.6, 67.1, 33.1, 65.2, 79.1, 83.7, 87.4,
29.0, 72.4, 59.1, 82.9, 85.3, 45.9, 23.7, 33.3, 8.4, 19.2, 81.8, 84.7, 72.2,
13.3, 3.7, 32.5, 49.3, 43.7, 61.8, 70.0, 54.1, 34.2, 93.1, 23.9, 11.4, 43.3,
84.3, 53.8, 4.0, 51.7, 25.2, 20.2, 2.4, 17.4, 28.9, 31.1, 44.7, 86.9, 86.0,
56.9, 57.9, 55.2, 83.2, 98.7, 94.4, 27.2, 18.2, 51.4, 59.6, 93.0, 53.0,
25.4, 21.0, 30.2, 79.7, 93.4, 17.9, 27.4, 98.7, 87.5, 29.0, 41.8, 21.8,
50.3, 41.5, 14.0, 10.2, 56.7, 53.4, 10.2, 87.6, 5.3, 4.2, 81.3, 88.5, 68.1,
45.8, 61.9, 59.0, 18.7, 44.4, 83.6, 73.7, 11.1, 92.5, 79.0, 35.3, 30.6, 7.4,
23.8, 24.9, 39.9, 29.0, 68.4, 99.2, 13.6, 26.9, 17.5, 27.8, 27.1, 72.5,
58.2, 17.5, 45.7, 90.2, 43.9, 66.2, 55.9, 21.2, 42.5, 37.4, 66.5, 11.0,
81.0, 99.8, 91.3, 24.6, 74.3, 15.4, 58.9, 37.8, 5.7, 97.1, 58.2, 13.8, 84.3,
85.1, 71.0, 16.2, 17.7, 70.7, 58.1, 67.4, 66.8, 46.3, 12.0, 26.7, 42.6,
46.2, 42.7, 86.5, 54.2, 20.1, 91.7, 45.6, 44.0, 66.2, 6.6, 100.0, 22.5,
39.5, 55.2, 93.0, 67.0, 22.8, 74.8, 94.5, 31.6, 31.4, 56.6, 27.7, 59.4,
88.1, 11.9, 91.7, 5.2, 70.7, 32.0, 88.9, 20.8, 26.7, 79.5, 11.5, 46.5, 86.9,
98.2, 70.5, 12.6, 34.1, 23.5, 16.4, 97.8, 52.0, 92.9, 25.8, 85.0, 21.4, 9.0,
95.6, 4.0, 80.1, 15.6, 79.3, 64.6, 51.0, 4.1, 4.9, 83.9, 43.0, 68.9, 2.7,
95.7, 35.0, 94.3, 34.9, 91.8, 14.9, 40.6, 40.9, 13.0, 92.5, 60.3, 30.6,
93.5, 99.0, 44.3, 86.1, 88.2, 24.0, 11.3, 25.8, 59.8, 90.0, 48.7, 24.0, 9.7,
71.7, 39.0, 50.2, 1.1, 96.2, 66.2, 67.3, 24.3, 96.4, 38.2, 34.8, 18.5, 32.2,
46.1, 16.9, 60.1, 77.9, 71.7, 32.4, 89.4, 15.9, 70.9, 12.4, 61.2, 74.1,
91.0, 62.6, 13.3, 68.7, 98.9, 68.5, 72.6, 45.6, 99.4, 5.6, 56.2, 58.3, 98.9,
82.6, 88.5, 59.4, 50.8, 48.6, 52.7, 82.0, 31.3, 76.6, 8.8, 86.4, 81.6, 32.2,
49.7, 22.3, 79.0, 85.0, 51.6, 82.6, 18.8, 82.3, 78.5, 71.4, 74.3, 62.6,
57.3, 97.3, 2.8, 61.8, 90.6, 11.1, 20.4, 88.4, 68.0, 50.7, 84.3, 15.3, 92.5,
18.6, 72.5, 15.5, 8.6, 60.0, 91.6, 86.1, 69.6, 29.5, 10.3, 10.8, 28.7, 50.9,
50.5, 55.9, 80.8, 86.2, 27.8, 52.2, 1.9, 80.1, 14.3, 96.3, 77.9, 4.5, 55.9,
79.3, 56.5, 5.1, 11.8, 40.4, 73.8, 1.6, 45.8, 16.7, 73.0, 70.1, 50.0, 15.0,
47.8, 36.1, 75.0, 93.5, 79.5, 91.4, 84.2, 92.0, 3.3, 93.4, 90.6, 16.0, 50.4,
8.9, 89.6, 23.6, 51.3, 99.9, 95.5, 27.1, 86.3, 5.5, 38.1, 60.4, 35.9, 75.7,
87.9, 79.7, 81.0, 71.9, 80.3, 1.6, 99.7, 66.0, 27.0, 23.1, 77.4, 63.4, 41.2,
25.0, 99.1, 64.8, 23.7, 14.8, 89.7, 28.9, 84.6, 20.8, 39.4, 11.5, 22.6,
27.9, 72.0, 81.6, 69.3, 35.4, 94.3, 14.1, 86.2, 25.3, 93.7, 17.0, 25.2,
46.5, 90.3, 76.7, 18.4, 35.8, 54.0, 2.5, 87.6, 84.0, 89.9, 33.7, 59.2, 9.5,
20.9, 72.3, 28.6, 70.6, 79.6, 63.2, 45.2, 26.8, 9.2, 92.8, 47.7, 23.8, 66.9,
20.3, 95.8, 95.6, 38.1, 77.9, 57.2, 46.0, 70.4, 1.3, 84.4, 57.5, 64.5, 88.6,
82.1, 14.1, 63.0, 88.8, 30.8, 62.9, 54.1, 29.1, 46.2, 48.5, 5.8, 28.3, 25.4,
87.7, 10.9, 93.9, 88.2, 54.8, 7.2, 94.2, 89.1, 7.4, 83.2, 30.5, 25.3, 1.9,
84.7, 29.7, 17.7, 95.6, 38.6, 36.5, 45.9, 5.8, 21.4, 70.2, 98.0, 43.3, 26.8,
73.2, 32.0, 80.0, 94.3, 52.6, 64.3, 74.1, 11.4, 59.3, 30.0, 45.1, 8.5, 78.9,
25.9, 21.7, 89.7, 92.3, 2.9, 38.8, 85.9, 3.3, 74.4, 86.9, 62.7, 52.6, 96.9,
13.6, 19.7, 74.4, 64.4, 17.1, 89.3, 76.2, 55.8, 19.4, 63.9, 65.8, 97.3,
47.0, 6.9, 69.5, 82.6, 36.6, 6.7, 78.4, 3.1, 30.1, 51.0, 85.4, 47.1, 39.9,
57.7, 39.7, 51.3, 66.4, 26.6, 44.3, 3.2, 48.8, 30.3, 75.9, 79.0, 37.2, 52.3,
12.2, 3.9, 69.2, 50.9, 20.9, 59.8, 99.2, 57.1, 78.5, 80.5, 95.9, 13.7, 8.3,
8.2, 60.6, 12.7, 99.1, 94.8, 89.9, 60.0, 90.9, 47.5, 36.1, 85.0, 80.1, 70.3,
33.2, 24.7, 41.5, 4.0, 27.3, 33.4, 53.9, 83.1, 8.0, 15.2, 78.5
],
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627.0, 470.0, 770.0, 649.0, 455.0, 912.0, 146.0, 364.0, 664.0, 355.0, 166.0,
278.0, 200.0, 769.0, 820.0, 969.0, 135.0, 884.0, 792.0, 144.0, 919.0, 133.0,
731.0, 442.0, 626.0, 359.0, 844.0, 180.0, 874.0, 285.0, 608.0, 653.0, 789.0,
638.0, 795.0, 813.0, 272.0, 378.0, 260.0, 176.0, 882.0, 689.0, 777.0, 641.0,
167.0, 249.0, 906.0, 776.0, 662.0, 933.0, 810.0, 723.0, 905.0, 966.0, 923.0,
540.0, 649.0, 105.0, 983.0, 368.0, 945.0, 467.0, 118.0, 320.0, 762.0, 469.0,
899.0, 442.0, 358.0, 160.0, 147.0, 251.0, 301.0, 718.0, 823.0, 816.0, 158.0,
202.0, 824.0, 105.0, 268.0, 363.0, 542.0, 650.0, 665.0, 664.0, 206.0, 643.0,
746.0, 604.0, 450.0, 693.0, 847.0, 393.0, 138.0, 566.0, 554.0, 796.0, 701.0,
862.0, 133.0, 586.0, 383.0, 470.0, 675.0, 305.0, 969.0, 226.0, 456.0, 479.0,
993.0, 442.0, 686.0, 420.0, 145.0, 327.0, 411.0, 834.0, 526.0, 579.0, 696.0,
446.0, 299.0, 128.0, 848.0, 875.0, 303.0, 767.0, 216.0, 370.0, 156.0, 568.0,
939.0, 192.0, 430.0, 429.0, 187.0, 718.0, 307.0, 316.0, 573.0, 307.0, 713.0,
549.0, 818.0, 938.0, 423.0, 370.0, 434.0, 155.0, 355.0, 225.0, 332.0, 211.0,
223.0, 417.0, 438.0, 366.0, 759.0, 109.0, 689.0, 583.0, 680.0, 797.0, 653.0,
551.0, 408.0, 822.0, 504.0, 546.0, 905.0, 874.0, 303.0, 743.0, 220.0, 383.0,
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88
benchmark/result/data_300.json Normal file
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benchmark/result/data_50.json Normal file
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}

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11
benchmark/two_column/x_data00.json
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benchmark/two_column/x_data01.json
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benchmark/two_column/x_data02.json
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benchmark/two_column/x_data10.json
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benchmark/two_column/x_data11.json
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benchmark/two_column/x_data12.json
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benchmark/two_column/x_data20.json
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89
benchmark/two_column/x_data21.json
View File

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118
benchmark/two_column/x_data22.json
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benchmark/two_column/y_data00.json
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{
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265
examples/correlation/correlation.ipynb
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13
examples/correlation/data.json Normal file
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12
examples/correlation/x_vals.json
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17
examples/correlation/y_vals.json
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89
examples/covariance/covariance.ipynb
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23
examples/covariance/data.json Normal file
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1
examples/covariance/x_vals.json
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17
examples/covariance/y_vals.json
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{
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54
examples/geomean/data.json
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examples/geomean/geomean.ipynb
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examples/geomean/geomean_OG.ipynb
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examples/harmomean/data.json
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{
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}

106
examples/harmomean/harmomean.ipynb
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examples/mean+median/data.json Normal file
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{
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72.7, 18.0, 42.2, 36.1, 76.7, 1.2, 96.4, 4.9, 92.0, 12.8, 28.2, 61.8, 56.9,
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87.2, 63.2, 81.8, 55.0, 59.7, 57.8, 43.4, 92.4, 66.9, 82.1, 51.0, 22.1,
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32.6, 22.2, 19.3, 49.1, 70.9, 43.9, 88.8, 56.3, 41.8, 90.3, 20.4, 80.4,
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89.6, 15.1, 45.0, 77.6, 30.5, 76.1, 46.9, 34.3, 65.1, 43.9, 91.6, 88.8, 8.9,
42.9, 11.8, 32.1, 20.1, 48.9, 79.7, 15.3, 45.4, 80.1, 73.1, 76.5, 52.4, 9.6,
41.9, 52.7, 55.1, 30.9, 83.7, 46.7, 39.3, 40.5, 52.4, 19.2, 25.8, 52.7,
81.0, 38.0, 54.5, 15.3, 64.3, 88.3, 49.8, 90.5, 90.4, 79.7, 87.3, 32.3,
11.9, 5.7, 33.6, 75.1, 65.9, 29.1, 39.4, 87.5, 3.3, 66.3, 79.0, 97.9, 69.6,
22.0, 62.8, 97.1, 90.4, 39.5, 11.7, 30.3, 18.9, 34.6, 6.6
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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,
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]
}

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examples/mean+median/data1.json
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{
"input_data": [
[
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33.8, 75.7, 96.8, 12.3, 18.4, 13.4, 6.0, 8.2, 25.8, 41.3, 68.5, 15.2,
74.7, 72.7, 18.0, 42.2, 36.1, 76.7, 1.2, 96.4, 4.9, 92.0, 12.8, 28.2,
61.8, 56.9, 44.3, 50.4, 81.6, 72.5, 12.9, 40.3, 12.8, 28.8, 36.3, 16.1,
68.4, 35.3, 79.2, 48.4, 97.1, 93.7, 77.0, 48.7, 93.7, 54.1, 65.4, 30.8,
34.4, 31.4, 78.7, 12.7, 90.7, 39.4, 86.0, 55.9, 6.8, 22.2, 65.3, 18.8,
7.1, 55.9, 38.6, 15.6, 59.2, 77.3, 76.9, 11.9, 19.9, 19.4, 54.3, 39.4,
4.0, 61.1, 16.8, 81.9, 49.3, 76.9, 19.2, 68.2, 54.4, 70.2, 89.8, 23.4,
67.5, 18.7, 10.8, 80.7, 80.3, 96.2, 62.3, 17.2, 23.0, 98.0, 19.1, 8.1,
36.2, 7.5, 55.9, 1.2, 56.8, 85.1, 18.9, 23.0, 13.5, 64.3, 9.1, 14.1, 14.1,
23.1, 73.2, 86.6, 39.1, 45.5, 85.0, 79.0, 15.8, 5.2, 81.5, 34.3, 24.3,
14.2, 84.6, 33.7, 86.3, 83.3, 62.8, 72.7, 14.7, 36.8, 92.5, 4.7, 30.0,
59.4, 57.6, 37.4, 22.0, 20.9, 61.6, 26.8, 47.1, 63.6, 6.0, 96.6, 61.2,
80.2, 59.3, 23.1, 29.3, 46.3, 89.2, 77.6, 83.2, 87.2, 63.2, 81.8, 55.0,
59.7, 57.8, 43.4, 92.4, 66.9, 82.1, 51.0, 22.1, 29.9, 41.0, 85.2, 61.5,
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70.9, 43.9, 88.8, 56.3, 41.8, 90.3, 20.4, 80.4, 36.4, 91.5, 69.6, 75.3,
92.4, 84.8, 17.7, 2.3, 41.3, 91.3, 68.6, 73.3, 62.5, 60.5, 73.5, 70.7,
77.5, 76.8, 98.1, 40.9, 66.3, 8.6, 48.9, 75.4, 14.7, 35.9, 89.6, 15.1,
45.0, 77.6, 30.5, 76.1, 46.9, 34.3, 65.1, 43.9, 91.6, 88.8, 8.9, 42.9,
11.8, 32.1, 20.1, 48.9, 79.7, 15.3, 45.4, 80.1, 73.1, 76.5, 52.4, 9.6,
41.9, 52.7, 55.1, 30.9, 83.7, 46.7, 39.3, 40.5, 52.4, 19.2, 25.8, 52.7,
81.0, 38.0, 54.5, 15.3, 64.3, 88.3, 49.8, 90.5, 90.4, 79.7, 87.3, 32.3,
11.9, 5.7, 33.6, 75.1, 65.9, 29.1, 39.4, 87.5, 3.3, 66.3, 79.0, 97.9,
69.6, 22.0, 62.8, 97.1, 90.4, 39.5, 11.7, 30.3, 18.9, 34.6, 6.6
]
]
}

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examples/mean+median/data2.json
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{
"input_data": [
[
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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
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]
}

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examples/mean/data.json
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{
"input_data": [
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63.0, 66.7, 2.5, 41.4, 35.6, 45.0, 13.7, 9.6, 16.6, 9.8, 20.3, 25.9, 71.9,
27.5, 30.9, 62.9, 18.6, 45.7, 2.4, 91.4, 16.2, 61.5, 41.4, 77.1, 53.2
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99.9, 75.2, 29.8, 19.4, 46.1, 94.8, 6.6, 94.5, 99.7, 1.6, 4.0, 86.7, 28.7,
63.0, 66.7, 2.5, 41.4, 35.6, 45.0, 13.7, 9.6, 16.6, 9.8, 20.3, 25.9, 71.9,
27.5, 30.9, 62.9, 18.6, 45.7, 2.4, 91.4, 16.2, 61.5, 41.4, 77.1, 53.2
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}

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examples/mean/data1.json
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{
"input_data": [[4, 5, 6.0]]
}

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examples/mean/data2.json
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{
"input_data": [[8, 9.0, 13]]
}

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examples/median/data.json
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{
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33.8, 75.7, 96.8, 12.3, 18.4, 13.4, 6.0, 8.2, 25.8, 41.3, 68.5, 15.2,
74.7, 72.7, 18.0, 42.2, 36.1, 76.7, 1.2, 96.4, 4.9, 92.0, 12.8, 28.2,
61.8, 56.9, 44.3, 50.4, 81.6, 72.5, 12.9, 40.3, 12.8, 28.8, 36.3, 16.1,
68.4, 35.3, 79.2, 48.4, 97.1, 93.7, 77.0, 48.7, 93.7, 54.1, 65.4, 30.8,
34.4, 31.4, 78.7, 12.7, 90.7, 39.4, 86.0, 55.9, 6.8, 22.2, 65.3, 18.8,
7.1, 55.9, 38.6, 15.6, 59.2, 77.3, 76.9, 11.9, 19.9, 19.4, 54.3, 39.4,
4.0, 61.1, 16.8, 81.9, 49.3, 76.9, 19.2, 68.2, 54.4, 70.2, 89.8, 23.4,
67.5, 18.7, 10.8, 80.7, 80.3, 96.2, 62.3, 17.2, 23.0, 98.0, 19.1, 8.1,
36.2, 7.5, 55.9, 1.2, 56.8, 85.1, 18.9, 23.0, 13.5, 64.3, 9.1, 14.1, 14.1,
23.1, 73.2, 86.6, 39.1, 45.5, 85.0, 79.0, 15.8, 5.2, 81.5, 34.3, 24.3,
14.2, 84.6, 33.7, 86.3, 83.3, 62.8, 72.7, 14.7, 36.8, 92.5, 4.7, 30.0,
59.4, 57.6, 37.4, 22.0, 20.9, 61.6, 26.8, 47.1, 63.6, 6.0, 96.6, 61.2,
80.2, 59.3, 23.1, 29.3, 46.3, 89.2, 77.6, 83.2, 87.2, 63.2, 81.8, 55.0,
59.7, 57.8, 43.4, 92.4, 66.9, 82.1, 51.0, 22.1, 29.9, 41.0, 85.2, 61.5,
14.6, 48.0, 52.7, 31.4, 83.9, 35.5, 77.3, 35.8, 32.6, 22.2, 19.3, 49.1,
70.9, 43.9, 88.8, 56.3, 41.8, 90.3, 20.4, 80.4, 36.4, 91.5, 69.6, 75.3,
92.4, 84.8, 17.7, 2.3, 41.3, 91.3, 68.6, 73.3, 62.5, 60.5, 73.5, 70.7,
77.5, 76.8, 98.1, 40.9, 66.3, 8.6, 48.9, 75.4, 14.7, 35.9, 89.6, 15.1,
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41.9, 52.7, 55.1, 30.9, 83.7, 46.7, 39.3, 40.5, 52.4, 19.2, 25.8, 52.7,
81.0, 38.0, 54.5, 15.3, 64.3, 88.3, 49.8, 90.5, 90.4, 79.7, 87.3, 32.3,
11.9, 5.7, 33.6, 75.1, 65.9, 29.1, 39.4, 87.5, 3.3, 66.3, 79.0, 97.9,
69.6, 22.0, 62.8, 97.1, 90.4, 39.5, 11.7, 30.3, 18.9, 34.6, 6.6
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examples/mode/data.json
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examples/pstdev/data.json
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examples/pvariance/data.json
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examples/regression/data.json Normal file
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81
examples/regression/regression.ipynb
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examples/regression/x1_vals.json
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examples/variance/data.json
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examples/where+geomean/data.json
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139
examples/where+geomean/where+geomean.ipynb
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54
examples/where+mean/data.json
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@@ -1,31 +1,29 @@
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Some files were not shown because too many files have changed in this diff Show More