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from .scenario_reducer import Scenario_reducer
import numpy as np
class Fast_forward_W2(Scenario_reducer):
'''
This class implements a scenario reducer that follows
a Fast Forward (FF) technique with a 2-norm metric.
Please notice that FF is preferred for n<N/4, where n is the
new reduced cardinality and N is the original one.
We assume a discrete uniform initial distribution on the scenarios.
'''
def __init__(self, initialSet):
# Assumption: all the scenarios must be different
self.initialSet = initialSet
self.N = initialSet.shape[1]
self.initProbs = (1/self.N)*np.ones(self.N) #room for generalization
def reduce(self, n_scenarios: int = 1):
# a = np.array([[0., 0., 2., 2.],[0., 2., 0., 2.]])
# b = np.array([0.25, 0.25, 0.25, 0.25])
# return a, b
"""
reduces the initial set of scenarios
"""
indxR = [] #indeces of the reduced set
probs_initial = self.initProbs.copy()
#### computation of the distance matrix
dist_mtrx = np.zeros((self.N,self.N))
for i in range(self.N):
for j in range(self.N):
dist_mtrx[i,j] = np.linalg.norm( self.initialSet[:,i] - self.initialSet[:,j] )
####
J_set = np.arange(self.N)
##Step 1
zeta = np.zeros(self.N)
for u in J_set:
tmpProb = probs_initial[u]
probs_initial[u] = 0 #set zero that element
zeta[u] = probs_initial @ dist_mtrx[:,u]
probs_initial[u] = tmpProb #restore the element
##first indx
indxR.append(np.nanargmin(zeta))
####
##Step i
for it in range(n_scenarios-1): #we already did the first
zeta = np.zeros(self.N) #new zeta
probs_initial[indxR] = 0 #set zero chosen elements
#new J_set
J_set = np.setdiff1d(J_set,indxR[-1]) # remove the last selected item
for u in J_set:
tmpProb = probs_initial[u]
probs_initial[u] = 0 #set zero that element
zeta[u] = probs_initial @ dist_mtrx[:,u]
probs_initial[u] = tmpProb #restore the element
#new selection
zeta[indxR] = np.nan #eliminated indx
indxR.append(np.nanargmin(zeta))
J_set = np.setdiff1d(J_set,indxR[-1])#last removed
####
##Probabilities redistribution
probs_initial = self.initProbs.copy() #re_int
probs_reduced = probs_initial[indxR] #probabilities in the reduced set
#closest scenario selection
#set diag NaN oth it would be the minimum
dist_mtrx[np.arange(self.N),np.arange(self.N)] = np.nan
indx_closest = lambda x: np.nanargmin(dist_mtrx[x,indxR])
for toDelete in J_set:
probs_reduced[indx_closest(toDelete)] += probs_initial[toDelete]
#new probs check
if round(np.sum(probs_reduced),2) != 1:
raise ValueError('new Probs must sum to one')
#returns the reduced set and the respective probabilities
return self.initialSet[:,indxR], probs_reduced
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