swutil/stochastic_processes.py
def fBrown(H,T,N,M,dW = None,cholesky = False):
    '''
    Sample fractional Brownian motion with differentiability index H 
    on interval [0,T] (H=1/2 yields standard Brownian motion)
    
    :param H: Differentiability, larger than 0
    :param T: Final time
    :param N: Number of time steps
    :param M: Number of samples
    :param dW: Driving noise, optional
    '''
    alpha = 0.5-H
    times = np.linspace(0, T, N)
    dt = T/(N-1)
    if cholesky:
        if dW is not None:
            raise ValueError('Cannot use provided dW if Cholesky method is used')
        times = times[1:]
        tdt = times/np.reshape(times,(-1,1))
        tdt[np.tril_indices(N-1,-1)]=0
        cov = np.reshape(times,(-1,1))**(1-2*alpha)*(1/(1-alpha))*(tdt-1)**(-alpha)*scipy.special.hyp2f1(alpha,1-alpha,2-alpha,1/(1-tdt))
        cov[0,:] = 0
        np.fill_diagonal(cov,times**(1-2*alpha)/(1-2*alpha))
        cov[np.tril_indices(N-1,-1)] = cov.T[np.tril_indices(N-1,-1)]
        L = scipy.linalg.cholesky(cov)
        return np.concatenate((np.zeros((1,M)),L.T@np.random.normal(size=(N-1,M))))
    if dW is None:
        dW = np.sqrt(dt)*np.random.normal(size=(N-1,M))
    if H == 0.5:
        return integral(dF = dW,cumulative = True)  
    a = 1/dt/(1-alpha)*((T-times[N-2::-1])**(1-alpha)-(T-times[:0:-1])**(1-alpha))#a is array that is convolved with dW. Values arise from conditioning integral pieces on dW 
    out = toeplitz_multiplication(a,np.zeros_like(a),dW[::-1])[::-1]
    out -=a[0]*dW#Redo last bit of defining integral with exact simulation below
    cov = np.array([[ dt**(1-2*alpha)/(1-2*alpha),dt**(1-alpha)/(1-alpha)],[dt**(1-alpha)/(1-alpha),dt]])
    var = cov[0,0]-cov[0,1]**2/cov[1,1]
    out += cov[0,1]/cov[1,1]*dW #Conditional mean
    out += np.sqrt(var)*np.random.normal(size = (N-1,M))#Conditional variance
    out = np.concatenate((np.zeros((1,M)),out))
    return out
def fBrown(H,T,N,M,dW = None,cholesky = False):
    '''
    Sample fractional Brownian motion with differentiability index H 
    on interval [0,T] (H=1/2 yields standard Brownian motion)
    
    :param H: Differentiability, larger than 0
    :param T: Final time
    :param N: Number of time steps
    :param M: Number of samples
    :param dW: Driving noise, optional
    '''
    alpha = 0.5-H
    times = np.linspace(0, T, N)
    dt = T/(N-1)
    if cholesky:
        if dW is not None:
            raise ValueError('Cannot use provided dW if Cholesky method is used')
        times = times[1:]
        tdt = times/np.reshape(times,(-1,1))
        tdt[np.tril_indices(N-1,-1)]=0
        cov = np.reshape(times,(-1,1))**(1-2*alpha)*(1/(1-alpha))*(tdt-1)**(-alpha)*scipy.special.hyp2f1(alpha,1-alpha,2-alpha,1/(1-tdt))
        cov[0,:] = 0
        np.fill_diagonal(cov,times**(1-2*alpha)/(1-2*alpha))
        cov[np.tril_indices(N-1,-1)] = cov.T[np.tril_indices(N-1,-1)]
        L = scipy.linalg.cholesky(cov)
        return np.concatenate((np.zeros((1,M)),L.T@np.random.normal(size=(N-1,M))))
    if dW is None:
        dW = np.sqrt(dt)*np.random.normal(size=(N-1,M))
    if H == 0.5:
        return integral(dF = dW,cumulative = True)  
    a = 1/dt/(1-alpha)*((T-times[N-2::-1])**(1-alpha)-(T-times[:0:-1])**(1-alpha))#a is array that is convolved with dW. Values arise from conditioning integral pieces on dW 
    out = toeplitz_multiplication(a,np.zeros_like(a),dW[::-1])[::-1]
    out -=a[0]*dW#Redo last bit of defining integral with exact simulation below
    cov = np.array([[ dt**(1-2*alpha)/(1-2*alpha),dt**(1-alpha)/(1-alpha)],[dt**(1-alpha)/(1-alpha),dt]])
    var = cov[0,0]-cov[0,1]**2/cov[1,1]
    out += cov[0,1]/cov[1,1]*dW #Conditional mean
    out += np.sqrt(var)*np.random.normal(size = (N-1,M))#Conditional variance
    out = np.concatenate((np.zeros((1,M)),out))
    return out
def fBrown ( H , T , N , M , dW = None , cholesky = False ) : alpha = 0.5 - H times = np . linspace ( 0 , T , N ) dt = T / ( N - 1 ) if cholesky : if dW is not None : raise ValueError ( 'Cannot use provided dW if Cholesky method is used' ) times = times [ 1 : ] tdt = times / np . reshape ( times , ( - 1 , 1 ) ) tdt [ np . tril_indices ( N - 1 , - 1 ) ] = 0 cov = np . reshape ( times , ( - 1 , 1 ) ) ** ( 1 - 2 * alpha ) * ( 1 / ( 1 - alpha ) ) * ( tdt - 1 ) ** ( - alpha ) * scipy . special . hyp2f1 ( alpha , 1 - alpha , 2 - alpha , 1 / ( 1 - tdt ) ) cov [ 0 , : ] = 0 np . fill_diagonal ( cov , times ** ( 1 - 2 * alpha ) / ( 1 - 2 * alpha ) ) cov [ np . tril_indices ( N - 1 , - 1 ) ] = cov . T [ np . tril_indices ( N - 1 , - 1 ) ] L = scipy . linalg . cholesky ( cov ) return np . concatenate ( ( np . zeros ( ( 1 , M ) ) , L . T @ np . random . normal ( size = ( N - 1 , M ) ) ) ) if dW is None : dW = np . sqrt ( dt ) * np . random . normal ( size = ( N - 1 , M ) ) if H == 0.5 : return integral ( dF = dW , cumulative = True ) a = 1 / dt / ( 1 - alpha ) * ( ( T - times [ N - 2 : : - 1 ] ) ** ( 1 - alpha ) - ( T - times [ : 0 : - 1 ] ) ** ( 1 - alpha ) ) #a is array that is convolved with dW. Values arise from conditioning integral pieces on dW  out = toeplitz_multiplication ( a , np . zeros_like ( a ) , dW [ : : - 1 ] ) [ : : - 1 ] out -= a [ 0 ] * dW #Redo last bit of defining integral with exact simulation below cov = np . array ( [ [ dt ** ( 1 - 2 * alpha ) / ( 1 - 2 * alpha ) , dt ** ( 1 - alpha ) / ( 1 - alpha ) ] , [ dt ** ( 1 - alpha ) / ( 1 - alpha ) , dt ] ] ) var = cov [ 0 , 0 ] - cov [ 0 , 1 ] ** 2 / cov [ 1 , 1 ] out += cov [ 0 , 1 ] / cov [ 1 , 1 ] * dW #Conditional mean out += np . sqrt ( var ) * np . random . normal ( size = ( N - 1 , M ) ) #Conditional variance out = np . concatenate ( ( np . zeros ( ( 1 , M ) ) , out ) ) return out
Sample fractional Brownian motion with differentiability index H 
    on interval [0,T] (H=1/2 yields standard Brownian motion)
    
    :param H: Differentiability, larger than 0
    :param T: Final time
    :param N: Number of time steps
    :param M: Number of samples
    :param dW: Driving noise, optional
Sample fractional Brownian motion with differentiability index H on interval [ 0 T ] ( H = 1 / 2 yields standard Brownian motion ) : param H : Differentiability larger than 0 : param T : Final time : param N : Number of time steps : param M : Number of samples : param dW : Driving noise optional