我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用numpy.kaiser()。
def local_kaiser3(vec): return(kaiser(len(vec),3*pi))
def iFFT(Y, output_length=None, window=False): """ Inverse real-valued Fourier Transform Parameters ---------- Y : array_like Frequency domain data [Nsignals x Nbins] output_length : int, optional Lenght of returned time-domain signal (Default: 2 x len(Y) + 1) win : boolean, optional Weights the resulting time-domain signal with a Hann Returns ------- y : array_like Reconstructed time-domain signal """ Y = _np.atleast_2d(Y) y = _np.fft.irfft(Y, n=output_length) if window: if window not in {'hann', 'hamming', 'blackman', 'kaiser'}: raise ValueError('Selected window must be one of hann, hamming, blackman or kaiser') no_of_signals, no_of_samples = y.shape if window == 'hann': window_array = _np.hanning(no_of_samples) elif window == 'hamming': window_array = _np.hamming(no_of_samples) elif window == 'blackman': window_array = _np.blackman(no_of_samples) elif window == 'kaiser': window_array = _np.kaiser(no_of_samples, 3) y = window_array * y return y
def smooth_curve(x): """????????????????????? ???http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html """ window_len = 11 s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]] w = np.kaiser(window_len, 2) y = np.convolve(w / w.sum(), s, mode='valid') return y[5:len(y) - 5]
def smooth_curve(x): """????????????????????? ???http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html """ window_len = 11 s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]] w = np.kaiser(window_len, 2) y = np.convolve(w/w.sum(), s, mode='valid') return y[5:len(y)-5]
def smooth_curve(x): """ Used to smooth the graph of the loss function reference:http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html """ window_len = 11 s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]] w = np.kaiser(window_len, 2) y = np.convolve(w/w.sum(), s, mode='valid') return y[5:len(y)-5]
