implicit_ODE_With_Jac.py 文件源码

def derivative_G(propensities,V,X,w,deter_vector,stoc_positions, positions, valid,jac):

    # just the deterministics
    X_d = X[deter_vector,:].copy()
    temp_eta = np.zeros((np.sum(deter_vector),X.shape[1]))
    j = 0
    for i in range(len(stoc_positions)):
        # If x-\nu_i is non zero
        if stoc_positions[i] == True:

            if np.sum(valid[:,j]) != 0:
                #print(" X shape: " + str(X.shape))
                #print(" w shape: " + str(w.shape))
                #print("test :" + str(map(propensities[i],*X[:,positions[valid[:,j]][:,j]])))

                # original Terms
                temp_eta[:,valid[:,j]] += (X_d[:,positions[valid[:,j]][:,j]] 
                                - X_d[:,valid[:,j]] +
                                V[i][deter_vector][:,np.newaxis]
                              )*map(propensities[i],* X[:,positions[valid[:,j]][:,j]])*w[positions[valid[:,j]][:,j]]

                # Correction terms
                # x terms
                temp_eta[:,:] -= jac(X,deter_vector,i)*w[np.newaxis,:] # these should be all the terms which are minusing out.
                # x-v_j term.
                temp_eta[:,valid[:,j]] += jac(X[:,positions[valid[:,j]][:,j]],deter_vector,i)*w[positions[valid[:,j]][:,j]][np.newaxis,:]



            j += 1
        else:
            temp_eta[:,:] += (V[i][deter_vector][:,np.newaxis])*map(propensities[i],* X)*w

    #return_X = np.zeros(X.shape)
    #return_X[deter_vector,:] = temp_eta
    #return_X[np.invert(deter_vector),:] = X[np.invert(deter_vector),:].copy()
    return temp_eta