効率的な方法

Question

私はskfuzzyでファジィ推論システムの構築に取り組んでいると私は私のコードをスピードアップする方法を見つける必要があります。

import skfuzzy as fuzz 
from skfuzzy import control as ctrl 
import numpy as np 


def FIS(s, r): 
    #Generate universe variables 
    a = ctrl.Antecedent(np.arange(0, 70.1, 0.1), 'a') 
    b = ctrl.Antecedent(np.arange(0, 6.01, 0.01), 'b') 
    c = ctrl.Consequent(np.arange(0, 12.01, 0.01), 'c') 

    #Generate fuzzy membership functions 
    #a 
    a['l'] = fuzz.trapmf(a.universe, [0.0, 0.0, 3.0, 6.0]) 
    a['m'] = fuzz.trapmf(a.universe,[3.0, 6.0, 16.0, 24.0]) 
    a['h'] = fuzz.trapmf(a.universe, [16.0, 24.0, 30.0, 45.0]) 
    a['e'] = fuzz.trapmf(a.universe, [30.0, 45.0, 70.0, 70.0]) 

    #b 
    b['l'] = fuzz.trapmf(b.universe, [0, 0, 0.01, 0.02]) 
    b['m'] = fuzz.trapmf(b.universe,[0.01, 0.02, 0.03, 0.05]) 
    b['h'] = fuzz.trapmf(b.universe, [0.03, 0.05, 0.10, 0.12]) 
    b['e'] = fuzz.trapmf(b.universe, [0.10, 0.12, 6.00, 6.00]) 

    #c 
    c['l'] = fuzz.trapmf(c.universe, [0, 0, 0.01, 0.02]) 
    c['m'] = fuzz.trapmf(c.universe,[0.01, 0.02, 0.04, 0.05]) 
    c['h'] = fuzz.trapmf(c.universe, [0.04, 0.05, 0.10, 0.20]) 
    c['e'] = fuzz.trapmf(c.universe, [0.10, 0.20, 12.00, 12.00]) 

    #FUZZY RULES 
    rule1 = ctrl.Rule(a['l'] & b['l'], c['l']) 
    rule2 = ctrl.Rule(a['l'] & b['m'], c['m']) 
    rule3 = ctrl.Rule(a['l'] & b['h'], c['h']) 
    rule4 = ctrl.Rule(a['m'] & b['l'], c['m']) 
    rule5 = ctrl.Rule(a['m'] & b['m'], c['m']) 
    rule6 = ctrl.Rule(a['m'] & b['h'], c['h']) 
    rule7 = ctrl.Rule(a['h'] & b['l'], c['h']) 
    rule8 = ctrl.Rule(a['h'] & b['m'], c['h']) 
    rule9 = ctrl.Rule(a['h'] & b['h'], c['e']) 
    rule10 = ctrl.Rule(a['e'], c['e']) 
    rule11 = ctrl.Rule(b['e'], c['e']) 

    #CONTROL SYSTEM 
    c_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6, 
    rule7, rule8, rule9, rule10, rule11]) 
    c_simulation = ctrl.ControlSystemSimulation(c_ctrl) 

    c_simulation.input['a'] = s 
    c_simulation.input['b'] = r 

    c_simulation.compute() 

    value = c_simulation.output['c'] 

    return value 

#Fake data 
s_data = np.random.RandomState(1234567890) 
s_data = s_data.randint(0, 70, size=600000) 

r_data = np.random.random_sample(600000) 

vec1 = s_data.flatten().astype('float') 
vec2 = r_data.flatten().astype('float') 

#pre allocate output array 
cert = np.zeros(np.shape(vec1))*np.nan 

#Find index of all finite elements of the array 
ind = np.where(np.isfinite(vec1))[0] 

# classify 
for k in xrange(len(ind)): 
    cert[ind[k]] = FIS(vec1[ind[k]], vec2[ind[k]]) 

私の計算が10以上を取っています完了までに+時間。 forループなしでこれらの計算を実行するにはどうすればよいですか？理想的には、私はnumpyソリューションを探していましたが、私は代替ソリューションについています。

Answer 1

私は上記の質問に対する優れた答えのために、scikit-fuzzy googleグループからJDWarnerにクレジットを与えたいと思います。私は他のscikit-fuzzyユーザーがこの投稿が将来有用であると思うでしょう。

https://groups.google.com/forum/#!topic/scikit-fuzzy/AOO0iNuHEuo