问访问附加到训练类的数据时的多cpu使用率

EN

from traits.api import HasTraits, List, Int, Enum, Instance
import numpy as np
import psutil
from itertools import combinations

"""
Small example of high CPU usage by traited classes
"""

class DataStorage(HasTraits):
    nsamples = Int(2000)
    samples  = List

    def _samples_default(self):
        return np.random.randn(self.nsamples,2000).tolist()

    def sample_samples(self,indices):
        """ return a 2D array of data at indices """
        return np.array(
                [self.samples[i] for i in indices])

class DataAccessor(HasTraits):
    """ Class that grabs data and computes something """
    measure = Enum("correlation","covariance")
    data_source = Instance(DataStorage,())

    def compute_measure(self,indices):
        """ example of some computation """
        samples = self.data_source.sample_samples(indices)
        percentage = psutil.cpu_percent(interval=0, percpu=True)
        if self.measure == "correlation":
            result = np.corrcoef(samples)
        elif self.measure == "covariance":
            result = np.cov(samples)
        return percentage

# Run a simulation to see cpu usage
analyzer = DataAccessor()
usage = []
n_iterations = 0
max_iterations = 500
for combo in combinations(np.arange(2000),500):
    # evaluate the measurement on a subset of the data
    usage.append(analyzer.compute_measure(combo))
    n_iterations += 1
    if n_iterations > max_iterations:
        break
    print n_iterations

use_percents = np.array(usage).T

>>> use_percents.mean(1)

array([ 67.05548902,  67.06906188,  66.89041916,  67.28942116,
        66.69421158,  67.61437126,  99.8007984 ,  67.31996008])