significantly improved time measurements
This commit is contained in:
parent
0cd2dfa8b4
commit
debcc57738
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@ -2,12 +2,13 @@ from collections import deque
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import matplotlib.pyplot as plt
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import numpy as np
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import json
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from random import shuffle
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from pyqcs import State, H, X, S, CZ, M, list_to_circuit
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from pyqcs.graph.state import GraphState
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from pyqcs.util.random_circuits import random_circuit
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from measure_circuit import execution_statistics
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from measure_circuit import execution_statistics, measure_all
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def S_with_extra_arg(act, i):
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return S(act)
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@ -18,21 +19,35 @@ def test_scaling_qbits(state_factory
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, ngates_per_qbit
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, ncircuits
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, **kwargs):
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results = deque()
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trials = deque()
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for qbits in range(nstart, nstop):
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N = (nstop - nstart)
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print()
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for n, qbits in enumerate(range(nstart, nstop)):
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print(f"generating test data... {int(n/N * 100)} %", end="\r", flush=True)
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measurement_circuit = list_to_circuit([M(i) for i in range(qbits)])
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circuits = [random_circuit(qbits, ngates_per_qbit * qbits, X, H, S_with_extra_arg, CZ)
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| measurement_circuit
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for _ in range(ncircuits)]
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state = state_factory(qbits)
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for circuit in circuits:
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trials.append((qbits, circuit, state))
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print("generating test data... done ")
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print("running test with", qbits, "qbits")
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N, avg, std_dev = execution_statistics(circuits, state, scale=qbits, **kwargs)
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print("randomizing tests...", end="", flush=True)
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shuffle(trials)
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print(" done")
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results = measure_all(trials, **kwargs)
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N, avg, std_dev = execution_statistics(results, scale={i:i for i in range(nstart, nstop)}, **kwargs)
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nqbits = [[i] for i in sorted(results.keys())]
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N = [[i] for i in N]
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avg = [[i] for i in avg]
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std_dev = [[i] for i in std_dev]
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return np.hstack([nqbits, N, avg, std_dev])
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results.append([qbits, N, avg, std_dev])
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return np.array(results, dtype=np.double)
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if __name__ == "__main__":
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nstart = 4
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@ -1,5 +1,5 @@
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import timeit
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from collections import deque
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from collections import deque, defaultdict
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import numpy as np
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def measure_execution_time(circuit, state, repeat=100, **kwargs):
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@ -14,17 +14,35 @@ def measure_execution_time(circuit, state, repeat=100, **kwargs):
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results.append(stop - start)
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return min(results)
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def measure_all(input_circuits_and_states, **kwargs):
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results = defaultdict(deque)
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def execution_statistics(circuits, state, scale=1, **kwargs):
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results = deque()
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N = len(input_circuits_and_states)
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for n, (key, circuit, state) in enumerate(input_circuits_and_states):
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print(f"running tests... {int(n/N * 100)} %", end="\r", flush=True)
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results[key].append(measure_execution_time(circuit, state, **kwargs))
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print("running tests... done ")
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return results
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for circuit in circuits:
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results.append(measure_execution_time(circuit, state, **kwargs))
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results = np.array(results, dtype=np.double)
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results /= scale
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N = len(results)
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avg = np.average(results)
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std_dev = np.std(results) / np.sqrt(N)
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def execution_statistics(results, scale=1, **kwargs):
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Ns = deque()
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avgs = deque()
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std_devs = deque()
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for k,v in sorted(results.items(), key=lambda x: x[0]):
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result = np.array(v, dtype=np.double)
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if(scale is not None):
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result /= scale[k]
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N = len(result)
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avg = np.average(result)
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std_dev = np.std(result) / np.sqrt(N)
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return N, avg, std_dev
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Ns.append(N)
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avgs.append(avg)
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std_devs.append(std_dev)
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Ns = np.array(Ns).astype(np.int64)
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avgs = np.array(avgs).astype(np.double)
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std_devs = np.array(std_devs).astype(np.double)
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return Ns, avgs, std_devs
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@ -1,12 +1,12 @@
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@ -1,12 +1,12 @@
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@ -1,48 +1,48 @@
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||||
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|
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|||
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|
@ -1,48 +1,48 @@
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2.400000000000000000e+03 1.000000000000000000e+02 9.922618439886718433e-03 8.657328357419615412e-06
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2.600000000000000000e+03 1.000000000000000000e+02 1.068992346969025592e-02 9.836075290728506446e-06
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7.500000000000000000e+02 1.000000000000000000e+02 3.204109490288828757e-03 2.401836261014467913e-06
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1.000000000000000000e+03 1.000000000000000000e+02 4.189837399717361716e-03 3.086862551918502448e-06
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1.050000000000000000e+03 1.000000000000000000e+02 4.370275900182605394e-03 3.099666612100548059e-06
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1.100000000000000000e+03 1.000000000000000000e+02 4.568666459781525226e-03 3.082617759197979633e-06
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1.700000000000000000e+03 1.000000000000000000e+02 6.941937389819940671e-03 4.995649399597023781e-06
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1.800000000000000000e+03 1.000000000000000000e+02 7.331140879796293856e-03 4.733622553888157934e-06
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2.100000000000000000e+03 1.000000000000000000e+02 8.517122190096416273e-03 5.625107381070845126e-06
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2.150000000000000000e+03 1.000000000000000000e+02 8.716500719856412616e-03 6.102662149448616443e-06
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2.200000000000000000e+03 1.000000000000000000e+02 8.933952380248228950e-03 6.092750330423816700e-06
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2.250000000000000000e+03 1.000000000000000000e+02 9.122699559629837002e-03 6.809876512233571178e-06
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||||
2.300000000000000000e+03 1.000000000000000000e+02 9.308247290100553351e-03 5.584932520477454584e-06
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||||
2.350000000000000000e+03 1.000000000000000000e+02 9.520456349891901968e-03 5.308699344716883628e-06
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||||
2.400000000000000000e+03 1.000000000000000000e+02 9.719488490081857127e-03 5.651934146065065715e-06
|
||||
2.450000000000000000e+03 1.000000000000000000e+02 9.917835799969907870e-03 6.118419019078714142e-06
|
||||
2.500000000000000000e+03 1.000000000000000000e+02 1.013053042981482653e-02 9.850550105507370965e-06
|
||||
2.550000000000000000e+03 1.000000000000000000e+02 1.031325545991421642e-02 7.359933188585473559e-06
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||||
2.600000000000000000e+03 1.000000000000000000e+02 1.051238634987385068e-02 5.962180914305465779e-06
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||||
2.650000000000000000e+03 1.000000000000000000e+02 1.074106708972976700e-02 7.587020510527051451e-06
|
||||
2.700000000000000000e+03 1.000000000000000000e+02 1.094498464983189451e-02 1.086042797471184508e-05
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||||
2.750000000000000000e+03 1.000000000000000000e+02 1.112320873024145833e-02 9.348830436389878298e-06
|
||||
|
|
|
|||
|
|
|
@ -2,12 +2,13 @@ from collections import deque
|
|||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import json
|
||||
from random import shuffle
|
||||
|
||||
from pyqcs import State, H, X, S, CZ, M, list_to_circuit
|
||||
from pyqcs.graph.state import GraphState
|
||||
from pyqcs.util.random_circuits import random_circuit
|
||||
|
||||
from measure_circuit import execution_statistics
|
||||
from measure_circuit import execution_statistics, measure_all
|
||||
|
||||
def S_with_extra_arg(act, i):
|
||||
return S(act)
|
||||
|
|
@ -19,20 +20,33 @@ def test_scaling_circuits(state_factory
|
|||
, nqbits
|
||||
, ncircuits
|
||||
, **kwargs):
|
||||
results = deque()
|
||||
trials = deque()
|
||||
|
||||
for ngates in range(nstart, nstop, step):
|
||||
N = (nstop - nstart) / step
|
||||
print()
|
||||
for n, ngates in enumerate(range(nstart, nstop, step)):
|
||||
print(f"generating test data... {int(n/N * 100)} %", end="\r", flush=True)
|
||||
measurement_circuit = list_to_circuit([M(i) for i in range(nqbits)])
|
||||
circuits = [random_circuit(nqbits, ngates, X, H, S_with_extra_arg, CZ)
|
||||
| measurement_circuit
|
||||
for _ in range(ncircuits)]
|
||||
state = state_factory(nqbits)
|
||||
for circuit in circuits:
|
||||
trials.append((ngates, circuit, state))
|
||||
print("generating test data... done ")
|
||||
|
||||
print("running test with", ngates, "gates on", nqbits, "qbits")
|
||||
print("randomizing tests...", end="", flush=True)
|
||||
shuffle(trials)
|
||||
print(" done")
|
||||
|
||||
N, avg, std_dev = execution_statistics(circuits, state, scale=1, **kwargs)
|
||||
results.append([ngates, N, avg, std_dev])
|
||||
results = measure_all(trials, **kwargs)
|
||||
N, avg, std_dev = execution_statistics(results, scale=1, **kwargs)
|
||||
ngates = [[i] for i in sorted(results.keys())]
|
||||
N = [[i] for i in N]
|
||||
avg = [[i] for i in avg]
|
||||
std_dev = [[i] for i in std_dev]
|
||||
|
||||
return np.array(results, dtype=np.double)
|
||||
return np.hstack([ngates, N, avg, std_dev])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -2,12 +2,13 @@ from collections import deque
|
|||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import json
|
||||
from random import shuffle
|
||||
|
||||
from pyqcs import State, H, X, S, CZ, M, list_to_circuit
|
||||
from pyqcs.graph.state import GraphState
|
||||
from pyqcs.util.random_circuits import random_circuit
|
||||
|
||||
from measure_circuit import execution_statistics
|
||||
from measure_circuit import execution_statistics, measure_all
|
||||
|
||||
def S_with_extra_arg(act, i):
|
||||
return S(act)
|
||||
|
|
@ -19,21 +20,33 @@ def test_scaling_circuits(state_factory
|
|||
, nqbits
|
||||
, ncircuits
|
||||
, **kwargs):
|
||||
results = deque()
|
||||
trials = deque()
|
||||
|
||||
for ngates in range(nstart, nstop, step):
|
||||
N = (nstop - nstart) / step
|
||||
print()
|
||||
for n, ngates in enumerate(range(nstart, nstop, step)):
|
||||
print(f"generating test data... {int(n/N * 100)} %", end="\r", flush=True)
|
||||
measurement_circuit = list_to_circuit([M(i) for i in range(nqbits)])
|
||||
circuits = [random_circuit(nqbits, ngates, M, H, S_with_extra_arg, CZ)
|
||||
| measurement_circuit
|
||||
for _ in range(ncircuits)]
|
||||
state = state_factory(nqbits)
|
||||
for circuit in circuits:
|
||||
trials.append((ngates, circuit, state))
|
||||
print("generating test data... done ")
|
||||
|
||||
print("running test with", ngates, "gates on", nqbits, "qbits")
|
||||
print("randomizing tests...", end="", flush=True)
|
||||
shuffle(trials)
|
||||
print(" done")
|
||||
|
||||
N, avg, std_dev = execution_statistics(circuits, state, scale=1, **kwargs)
|
||||
results.append([ngates, N, avg, std_dev])
|
||||
results = measure_all(trials, **kwargs)
|
||||
N, avg, std_dev = execution_statistics(results, scale=1, **kwargs)
|
||||
ngates = [[i] for i in sorted(results.keys())]
|
||||
N = [[i] for i in N]
|
||||
avg = [[i] for i in avg]
|
||||
std_dev = [[i] for i in std_dev]
|
||||
|
||||
return np.array(results, dtype=np.double)
|
||||
return np.hstack([ngates, N, avg, std_dev])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import timeit
|
||||
from collections import deque
|
||||
from collections import deque, defaultdict
|
||||
import numpy as np
|
||||
|
||||
def measure_execution_time(circuit, state, repeat=100, **kwargs):
|
||||
|
|
@ -14,17 +14,35 @@ def measure_execution_time(circuit, state, repeat=100, **kwargs):
|
|||
results.append(stop - start)
|
||||
return min(results)
|
||||
|
||||
def measure_all(input_circuits_and_states, **kwargs):
|
||||
results = defaultdict(deque)
|
||||
|
||||
def execution_statistics(circuits, state, scale=1, **kwargs):
|
||||
results = deque()
|
||||
N = len(input_circuits_and_states)
|
||||
for n, (key, circuit, state) in enumerate(input_circuits_and_states):
|
||||
print(f"running tests... {int(n/N * 100)} %", end="\r", flush=True)
|
||||
results[key].append(measure_execution_time(circuit, state, **kwargs))
|
||||
print("running tests... done ")
|
||||
return results
|
||||
|
||||
for circuit in circuits:
|
||||
results.append(measure_execution_time(circuit, state, **kwargs))
|
||||
|
||||
results = np.array(results, dtype=np.double)
|
||||
results /= scale
|
||||
N = len(results)
|
||||
avg = np.average(results)
|
||||
std_dev = np.std(results) / np.sqrt(N)
|
||||
def execution_statistics(results, scale=None, **kwargs):
|
||||
Ns = deque()
|
||||
avgs = deque()
|
||||
std_devs = deque()
|
||||
for k,v in sorted(results.items(), key=lambda x: x[0]):
|
||||
result = np.array(v, dtype=np.double)
|
||||
if(scale is not None):
|
||||
result /= scale[k]
|
||||
N = len(result)
|
||||
avg = np.average(result)
|
||||
std_dev = np.std(result) / np.sqrt(N)
|
||||
|
||||
return N, avg, std_dev
|
||||
Ns.append(N)
|
||||
avgs.append(avg)
|
||||
std_devs.append(std_dev)
|
||||
|
||||
Ns = np.array(Ns).astype(np.int64)
|
||||
avgs = np.array(avgs).astype(np.double)
|
||||
std_devs = np.array(std_devs).astype(np.double)
|
||||
|
||||
return Ns, avgs, std_devs
|
||||
|
|
|
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Reference in New Issue