import torch
import numpy as np


#
#
#   Car: 11D objective, 10 constraints
#
#   Reference:
#     Gandomi AH, Yang XS, Alavi AH (2011) Mixed
#     variable structural optimization using firefly
#     algorithm. Computers & Structures 89(23-
#     24):2325–2336
#
#



def Car(individuals): 

    assert torch.is_tensor(individuals) and individuals.size(1) == 11, "Input must be an n-by-11 PyTorch tensor."
    
    n = individuals.size(0)
    
    fx = torch.zeros((n,1))
    gx1 = torch.zeros((n,1))
    gx2 = torch.zeros((n,1))
    gx3 = torch.zeros((n,1))
    gx4 = torch.zeros((n,1))
    gx5 = torch.zeros((n,1))
    gx6 = torch.zeros((n,1))
    gx7 = torch.zeros((n,1))
    gx8 = torch.zeros((n,1))
    gx9 = torch.zeros((n,1))
    gx10 = torch.zeros((n,1))
    gx11 = torch.zeros((n,1))
    

    n = individuals.size(0)
    
    # Set function and constraints here:
    for i in range(n):
        
        x = individuals[i,:]
        
        x1 = x[0]
        x2 = x[1]
        x3 = x[2]
        x4 = x[3]
        x5 = x[4]
        x6 = x[5]
        x7 = x[6]
        x8 = x[7]
        x9 = x[8]
        x10 = x[9]
        x11 = x[10]

        
        
        
        ## Negative sign to make it a maximization problem
        test_function = - ( 1.98 + 4.90*x1 + 6.67*x2 + 6.98*x3 + 4.01*x4 + 1.78*x5 + 2.73*x7 )

        
        ## Calculate constraints terms 
        g1 = 1.16 - 0.3717*x2*x4 - 0.00931*x2*x10 - 0.484*x3*x9 + 0.01343*x6*x10   -1
        
        g2 = (0.261 - 0.0159*x1*x2 - 0.188*x1*x8 
              - 0.019*x2*x7 + 0.0144*x3*x5 + 0.0008757*x5*x10
              + 0.08045*x6*x9 + 0.00139*x8*x11 + 0.00001575*x10*x11)        -0.9
        
        g3 = (0.214 + 0.00817*x5 - 0.131*x1*x8 - 0.0704*x1*x9 + 0.03099*x2*x6
              -0.018*x2*x7 + 0.0208*x3*x8 + 0.121*x3*x9 - 0.00364*x5*x6
              +0.0007715*x5*x10 - 0.0005354*x6*x10 + 0.00121*x8*x11)        -0.9
        
        g4 = 0.74 -0.061*x2 -0.163*x3*x8 +0.001232*x3*x10 -0.166*x7*x9 +0.227*x2*x2        -0.9
        
        g5 = 28.98 +3.818*x3-4.2*x1*x2+0.0207*x5*x10+6.63*x6*x9-7.7*x7*x8+0.32*x9*x10    -32
        
        g6 = 33.86 +2.95*x3+0.1792*x10-5.057*x1*x2-11.0*x2*x8-0.0215*x5*x10-9.98*x7*x8+22.0*x8*x9    -32
        
        g7 = 46.36 -9.9*x2-12.9*x1*x8+0.1107*x3*x10    -32
        
        g8 = 4.72 -0.5*x4-0.19*x2*x3-0.0122*x4*x10+0.009325*x6*x10+0.000191*x11**2     -4
        
        g9 = 10.58 -0.674*x1*x2-1.95*x2*x8+0.02054*x3*x10-0.0198*x4*x10+0.028*x6*x10     -9.9
        
        g10 = 16.45 -0.489*x3*x7-0.843*x5*x6+0.0432*x9*x10-0.0556*x9*x11-0.000786*x11**2     -15.7
        
        
        gx1[i] = g1
        gx2[i] = g2
        gx3[i] = g3
        gx4[i] = g4
        gx5[i] = g5
        gx6[i] = g6
        gx7[i] = g7
        gx8[i] = g8
        gx9[i] = g9
        gx10[i] = g10
        fx[i] = test_function
        
        
    gx = torch.cat((gx1, gx2, gx3, gx4, gx5, gx6, gx7, gx8, gx9, gx10), 1)


    
    return gx, fx




def Car_Scaling(X):

    assert torch.is_tensor(X) and X.size(1) == 11, "Input must be an n-by-11 PyTorch tensor."
    
    x1  = (X[:,0] * (1.5-0.5) + 0.5).reshape(X.shape[0],1)
    x2  = (X[:,1] * (1.35-0.45) + 0.45).reshape(X.shape[0],1)
    x3  = (X[:,2] * (1.5-0.5) + 0.5).reshape(X.shape[0],1)
    x4 = (X[:,3] * (1.5-0.5) + 0.5).reshape(X.shape[0],1)
    x5 = (X[:,4] * (1.5-0.5) + 0.5).reshape(X.shape[0],1)
    x6 = (X[:,5] * (1.5-0.5) + 0.5).reshape(X.shape[0],1)
    x7 = (X[:,6] * (1.5-0.5) + 0.5).reshape(X.shape[0],1)
    x8 = (X[:,7] * (0.345-0.192) + 0.192).reshape(X.shape[0],1)
    x9 = (X[:,8] * (0.345-0.192) + 0.192).reshape(X.shape[0],1)
    x10 = (X[:,9] * (-20)).reshape(X.shape[0],1)
    x11 = (X[:,10] * (-20)).reshape(X.shape[0],1)
    
    X_scaled = torch.cat((x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11), dim=1)
    return X_scaled






def Car_softpen(individuals): 

    assert torch.is_tensor(individuals) and individuals.size(1) == 11, "Input must be an n-by-11 PyTorch tensor."
    
    n = individuals.size(0)
    
    fx = torch.zeros((n,1))
    gx1 = torch.zeros((n,1))
    gx2 = torch.zeros((n,1))
    gx3 = torch.zeros((n,1))
    gx4 = torch.zeros((n,1))
    gx5 = torch.zeros((n,1))
    gx6 = torch.zeros((n,1))
    gx7 = torch.zeros((n,1))
    gx8 = torch.zeros((n,1))
    gx9 = torch.zeros((n,1))
    gx10 = torch.zeros((n,1))
    gx11 = torch.zeros((n,1))
    

    n = individuals.size(0)
    
    # Set function and constraints here:
    for i in range(n):
        
        x = individuals[i,:]
        
        x1 = x[0]
        x2 = x[1]
        x3 = x[2]
        x4 = x[3]
        x5 = x[4]
        x6 = x[5]
        x7 = x[6]
        x8 = x[7]
        x9 = x[8]
        x10 = x[9]
        x11 = x[10]

        
        
        
        ## Negative sign to make it a maximization problem
        test_function = - ( 1.98 + 4.90*x1 + 6.67*x2 + 6.98*x3 + 4.01*x4 + 1.78*x5 + 2.73*x7 )

        
        ## Calculate constraints terms 
        g1 = 1.16 - 0.3717*x2*x4 - 0.00931*x2*x10 - 0.484*x3*x9 + 0.01343*x6*x10   -1
        
        g2 = (0.261 - 0.0159*x1*x2 - 0.188*x1*x8 
              - 0.019*x2*x7 + 0.0144*x3*x5 + 0.0008757*x5*x10
              + 0.08045*x6*x9 + 0.00139*x8*x11 + 0.00001575*x10*x11)        -0.9
        
        g3 = (0.214 + 0.00817*x5 - 0.131*x1*x8 - 0.0704*x1*x9 + 0.03099*x2*x6
              -0.018*x2*x7 + 0.0208*x3*x8 + 0.121*x3*x9 - 0.00364*x5*x6
              +0.0007715*x5*x10 - 0.0005354*x6*x10 + 0.00121*x8*x11)        -0.9
        
        g4 = 0.74 -0.061*x2 -0.163*x3*x8 +0.001232*x3*x10 -0.166*x7*x9 +0.227*x2*x2        -0.9
        
        g5 = 28.98 +3.818*x3-4.2*x1*x2+0.0207*x5*x10+6.63*x6*x9-7.7*x7*x8+0.32*x9*x10    -32
        
        g6 = 33.86 +2.95*x3+0.1792*x10-5.057*x1*x2-11.0*x2*x8-0.0215*x5*x10-9.98*x7*x8+22.0*x8*x9    -32
        
        g7 = 46.36 -9.9*x2-12.9*x1*x8+0.1107*x3*x10    -32
        
        g8 = 4.72 -0.5*x4-0.19*x2*x3-0.0122*x4*x10+0.009325*x6*x10+0.000191*x11**2     -4
        
        g9 = 10.58 -0.674*x1*x2-1.95*x2*x8+0.02054*x3*x10-0.0198*x4*x10+0.028*x6*x10     -9.9
        
        g10 = 16.45 -0.489*x3*x7-0.843*x5*x6+0.0432*x9*x10-0.0556*x9*x11-0.000786*x11**2     -15.7
        
        
        gx1[i] = g1
        gx2[i] = g2
        gx3[i] = g3
        gx4[i] = g4
        gx5[i] = g5
        gx6[i] = g6
        gx7[i] = g7
        gx8[i] = g8
        gx9[i] = g9
        gx10[i] = g10
        fx[i] = test_function
        
        
    gx = torch.cat((gx1, gx2, gx3, gx4, gx5, gx6, gx7, gx8, gx9, gx10), 1)

    cost = gx
    cost[cost<0] = 0
    cost = cost.sum(dim=1).reshape(cost.shape[0], 1)
    fx = fx + cost


    
    return gx, fx