LA-MCTS-baselines/Bayesian-Optimization/functions.py [39:94]:
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        print("="*10)
        print("current best f(x):", self.curt_best)
        print("current best x:", np.around(self.curt_best_x, decimals=1))
        self.results.append(self.curt_best)
        self.counter += 1
        if len(self.results) % 100 == 0:
            self.dump_trace()
        
class Levy:
    def __init__(self, dims=1):
        self.dims    = dims
        self.lb      = -10 * np.ones(dims)
        self.ub      =  10 * np.ones(dims)
        self.counter = 0
        print("####dims:", dims)
        self.tracker = tracker('Levy'+str(dims))

    def __call__(self, x):
        x = np.array(x)
        self.counter += 1
        assert len(x) == self.dims
        assert x.ndim == 1
        assert np.all(x <= self.ub) and np.all(x >= self.lb)
        w = []
        for idx in range(0, len(x)):
            w.append( 1 + (x[idx] - 1) / 4 )
        w = np.array(w)
        
        term1 = ( np.sin( np.pi*w[0] ) )**2;
        
        term3 = ( w[-1] - 1 )**2 * ( 1 + ( np.sin( 2 * np.pi * w[-1] ) )**2 );
        
        
        term2 = 0;
        for idx in range(1, len(w) ):
            wi  = w[idx]
            new = (wi-1)**2 * ( 1 + 10 * ( np.sin( np.pi* wi + 1 ) )**2)
            term2 = term2 + new
        
        result = term1 + term2 + term3

        self.tracker.track( result, x )

        return result
    
        
class Ackley:
    def __init__(self, dims=3):
        self.dims    = dims
        self.lb      = -5 * np.ones(dims)
        self.ub      = 10 * np.ones(dims)
        self.counter = 0
        self.tracker = tracker('Ackley'+str(dims))
        

    def __call__(self, x):
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LA-MCTS-baselines/Nevergrad/functions.py [39:93]:
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        print("="*10)
        print("current best f(x):", self.curt_best)
        print("current best x:", np.around(self.curt_best_x, decimals=1))
        self.results.append(self.curt_best)
        self.counter += 1
        if len(self.results) % 100 == 0:
            self.dump_trace()            
    
class Levy:
    def __init__(self, dims=1):
        self.dims    = dims
        self.lb      = -10 * np.ones(dims)
        self.ub      =  10 * np.ones(dims)
        self.counter = 0
        print("####dims:", dims)
        self.tracker = tracker('Levy'+str(dims))

    def __call__(self, x):
        x = np.array(x)
        self.counter += 1
        assert len(x) == self.dims
        assert x.ndim == 1
        assert np.all(x <= self.ub) and np.all(x >= self.lb)
        w = []
        for idx in range(0, len(x)):
            w.append( 1 + (x[idx] - 1) / 4 )
        w = np.array(w)
        
        term1 = ( np.sin( np.pi*w[0] ) )**2;
        
        term3 = ( w[-1] - 1 )**2 * ( 1 + ( np.sin( 2 * np.pi * w[-1] ) )**2 );
        
        
        term2 = 0;
        for idx in range(1, len(w) ):
            wi  = w[idx]
            new = (wi-1)**2 * ( 1 + 10 * ( np.sin( np.pi* wi + 1 ) )**2)
            term2 = term2 + new
        
        result = term1 + term2 + term3

        self.tracker.track( result, x )

        return result
        
class Ackley:
    def __init__(self, dims=3):
        self.dims   = dims
        self.lb    = -5 * np.ones(dims)
        self.ub    =  10 * np.ones(dims)
        self.counter = 0
        self.tracker = tracker('Ackley'+str(dims))
        

    def __call__(self, x):
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