Deprecation warnings and JADE fix

NiaPy/algorithms/basic/es.py

@@ -326,7 +326,7 @@ def changeCount(self, c, cn):
 		"""
 		k = 0
 		for e in cn:
-			if e not in c: k += 1
+			if e not in c.tolist(): k += 1
 		return k
 
 	def mutateRand(self, pop, task):

NiaPy/algorithms/basic/fa.py

@@ -173,8 +173,8 @@ def runIteration(self, task, Fireflies, Intensity, xb, fxb, alpha, **dparams):
 		"""
 		alpha = self.alpha_new(task.nFES / self.NP, alpha)
 		Index = argsort(Intensity)
-		tmp = asarray([self.move_ffa(i, Fireflies[Index], Intensity[Index], Fireflies, alpha, task) for i in range(self.NP)])
-		Fireflies, evalF = asarray([tmp[i][0] for i in range(len(tmp))]), asarray([tmp[i][1] for i in range(len(tmp))])
+		tmp = asarray([self.move_ffa(i, Fireflies[Index], Intensity[Index], Fireflies, alpha, task) for i in range(self.NP)], dtype=object)
+		Fireflies, evalF = asarray([tmp[i][0] for i in range(len(tmp))], dtype=object), asarray([tmp[i][1] for i in range(len(tmp))])
 		Intensity[where(evalF)] = apply_along_axis(task.eval, 1, Fireflies[where(evalF)])
 		xb, fxb = self.getBest(Fireflies, Intensity, xb, fxb)
 		return Fireflies, Intensity, xb, fxb, {'alpha': alpha}

NiaPy/algorithms/modified/jade.py

@@ -1,7 +1,7 @@
 # encoding=utf8
 import logging
 
-from numpy import random as rand, concatenate, asarray, argsort
+from numpy import random as rand, concatenate, argsort, vstack
 
 from NiaPy.algorithms.basic.de import DifferentialEvolution
 
@@ -14,7 +14,7 @@
 	'CrossRandCurr2Pbest'
 ]
 
-def CrossRandCurr2Pbest(pop, ic, x_b, f, cr, p=0.2, arc=None, rnd=rand, *args):
+def CrossRandCurr2Pbest(pop, ic, fpop, f, cr, p=0.2, arc=None, rnd=rand, *args):
 	r"""Mutation strategy with crossover.
 
 	Mutation strategy uses two different random individuals from population to perform mutation.
@@ -25,7 +25,7 @@ def CrossRandCurr2Pbest(pop, ic, x_b, f, cr, p=0.2, arc=None, rnd=rand, *args):
 	Args:
 		pop (numpy.ndarray): Current population.
 		ic (int): Index of current individual.
-		x_b (numpy.ndarray): Global best individual.
+		fpop (numpy.ndarray): Current population scores.
 		f (float): Scale factor.
 		cr (float): Crossover probability.
 		p (float): Procentage of best individuals to use.
@@ -40,18 +40,18 @@ def CrossRandCurr2Pbest(pop, ic, x_b, f, cr, p=0.2, arc=None, rnd=rand, *args):
 	pb = [1.0 / (len(pop) - 1) if i != ic else 0 for i in range(len(pop))] if len(pop) > 1 else None
 	r = rnd.choice(len(pop), 1, replace=not len(pop) >= 3, p=pb)
 	# Get pbest index
-	index, pi = argsort(pop), int(len(pop) * p)
+	index, pi = argsort(fpop), int(len(fpop) * p)
 	ppop = pop[index[:pi]]
 	pb = [1.0 / len(ppop) for i in range(pi)] if len(ppop) > 1 else None
 	rp = rnd.choice(pi, 1, replace=not len(ppop) >= 1, p=pb)
 	# Get union population and archive index
 	apop = concatenate((pop, arc)) if arc is not None else pop
 	pb = [1.0 / (len(apop) - 1) if i != ic else 0 for i in range(len(apop))] if len(apop) > 1 else None
 	ra = rnd.choice(len(apop), 1, replace=not len(apop) >= 1, p=pb)
-	# Generate new positoin
+	# Generate new position
 	j = rnd.randint(len(pop[ic]))
-	x = [pop[ic][i] + f * (ppop[rp[0]][i] - pop[ic][i]) + f * (pop[r[0]][i] - apop[ra[0]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
-	return asarray(x)
+	x = [el + f * (ppop[rp[0]][elidx] - el) + f * (pop[r[0]][elidx] - apop[ra[0]][elidx]) if rnd.rand() < cr or elidx == j else el for elidx, el in enumerate(pop[ic])]
+	return vstack(x)
 
 class AdaptiveArchiveDifferentialEvolution(DifferentialEvolution):
 	r"""Implementation of Adaptive Differential Evolution With Optional External Archive algorithm.

NiaPy/algorithms/other/aso.py

@@ -399,7 +399,7 @@ def runIteration(self, task, X, X_f, xb, fxb, Xpb, Xpb_f, alpha, gamma, theta, r
 		"""
 		Xin = [self.getBestNeighbors(i, X, X_f, rs) for i in range(len(X))]
 		MP_c, MP_s, MP_p = asarray([self.FI(X_f[i], Xpb_f[i], fxb, alpha[i]) for i in range(len(X))]), asarray([self.EI(X_f[i], X_f[Xin[i]], gamma[i]) for i in range(len(X))]), asarray([self.II(X_f[i], Xpb_f[i], theta[i]) for i in range(len(X))])
-		Xtmp = asarray([self.Combination(X[i], Xpb[i], xb, X[self.randint(len(X), skip=[i])], MP_c[i], MP_s[i], MP_p[i], self.F, self.CR, task, self.Rand) for i in range(len(X))])
+		Xtmp = asarray([self.Combination(X[i], Xpb[i], xb, X[self.randint(len(X), skip=[i])], MP_c[i], MP_s[i], MP_p[i], self.F, self.CR, task, self.Rand) for i in range(len(X))], dtype=object)
 		X, X_f = asarray([Xtmp[i][0] for i in range(len(X))]), asarray([Xtmp[i][1] for i in range(len(X))])
 		Xpb, Xpb_f = self.uBestAndPBest(X, X_f, Xpb, Xpb_f)
 		xb, fxb = self.getBest(X, X_f, xb, fxb)

NiaPy/tests/test_jade.py

@@ -24,6 +24,7 @@ def test_function_fine(self):
 		xb, fxb = pop[ib].copy(), fpop[ib]
 		for i, x in enumerate(pop):
 			xn = CrossRandCurr2Pbest(pop, i, xb, self.F, self.CR, self.p, apop)
+			# Check that position array is updated
 			self.assertFalse(np.array_equal(x, xn))
 
 class JADETestCase(AlgorithmTestCase):