(T, dU, hyperparams)
return noise
Generate a T x dU gaussian-distributed noise vector. This will
    approximately have mean 0 and variance 1, ignoring smoothing.

    Args:
        T: Number of time steps.
        dU: Dimensionality of actions.
    Hyperparams:
        smooth: Whether or not to perform smoothing of noise.
        var : If smooth=True, applies a Gaussian filter with this
            variance.
        renorm : If smooth=True, renormalizes data to have variance 1
            after smoothing.
Generate a T x dU gaussian-distributed noise vector. This will
    approximately have mean 0 and variance 1, ignoring smoothing.
Generate a T x dU gaussian - distributed noise vector . This will approximately have mean 0 and variance 1 ignoring smoothing .
def generate_noise(T, dU, hyperparams):
    """
    Generate a T x dU gaussian-distributed noise vector. This will
    approximately have mean 0 and variance 1, ignoring smoothing.

    Args:
        T: Number of time steps.
        dU: Dimensionality of actions.
    Hyperparams:
        smooth: Whether or not to perform smoothing of noise.
        var : If smooth=True, applies a Gaussian filter with this
            variance.
        renorm : If smooth=True, renormalizes data to have variance 1
            after smoothing.
    """
    smooth, var = hyperparams['smooth_noise'], hyperparams['smooth_noise_var']
    renorm = hyperparams['smooth_noise_renormalize']
    noise = np.random.randn(T, dU)
    if smooth:
        # Smooth noise. This violates the controller assumption, but
        # might produce smoother motions.
        for i in range(dU):
            noise[:, i] = sp_ndimage.filters.gaussian_filter(noise[:, i], var)
        if renorm:
            variance = np.var(noise, axis=0)
            noise = noise / np.sqrt(variance)
    return noise
def generate_noise ( T , dU , hyperparams ) : smooth , var = hyperparams [ 'smooth_noise' ] , hyperparams [ 'smooth_noise_var' ] renorm = hyperparams [ 'smooth_noise_renormalize' ] noise = np . random . randn ( T , dU ) if smooth : # Smooth noise. This violates the controller assumption, but # might produce smoother motions. for i in range ( dU ) : noise [ : , i ] = sp_ndimage . filters . gaussian_filter ( noise [ : , i ] , var ) if renorm : variance = np . var ( noise , axis = 0 ) noise = noise / np . sqrt ( variance ) return noise