util.py 文件源码

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

    # 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)):
        ##pdb.set_trace()
        # 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]])))


                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]]
            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 return_X
    #return temp_eta