Fitting arbitraty function (model) with Python/Sicpy
0
I'm wondering if it's possible using scipy.optimize.curve_fit or any other different out-of-the-box method to fit some arbitrary defined fuction(model) like e.g.:
def model_smooth_ramp(x, x0, x1, a, b, s):
y = np.piecewise(x, [(x < x0), (x0 <= x) * (x < x1), (x >= x1)], [0, lambda x: (x - x0) *(1/(x1-x0)), 1])
return a * smooth(y, window_len=s) + b
Where:
def smooth(x, window_len=6, window='flat'):
if x.ndim != 1:
raise ValueError("smooth only accepts 1 dimension arrays.")
if x.size < window_len:
raise ValueError("Input vector needs to be bigger than window size.")
if window_len < 3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError("Window is one of: 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
s = np.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]]
if window == 'flat': # moving average
w = np.ones(window_len, 'd')
else:
w = eval('np.' + window + '(window_len)')
y = np.convolve(w / w.sum(), s, mode='valid')
return y[(int(window_len / 2 - 1)):-(int(window_len / 2))]
python scikit-learn scipy data-fitting model-fitting
asked 21 hours ago
meller92meller92
7310
add a comment |
0
I'm wondering if it's possible using scipy.optimize.curve_fit or any other different out-of-the-box method to fit some arbitrary defined fuction(model) like e.g.:
def model_smooth_ramp(x, x0, x1, a, b, s):
y = np.piecewise(x, [(x < x0), (x0 <= x) * (x < x1), (x >= x1)], [0, lambda x: (x - x0) *(1/(x1-x0)), 1])
return a * smooth(y, window_len=s) + b
Where:
def smooth(x, window_len=6, window='flat'):
if x.ndim != 1:
raise ValueError("smooth only accepts 1 dimension arrays.")
if x.size < window_len:
raise ValueError("Input vector needs to be bigger than window size.")
if window_len < 3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError("Window is one of: 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
s = np.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]]
if window == 'flat': # moving average
w = np.ones(window_len, 'd')
else:
w = eval('np.' + window + '(window_len)')
y = np.convolve(w / w.sum(), s, mode='valid')
return y[(int(window_len / 2 - 1)):-(int(window_len / 2))]
python scikit-learn scipy data-fitting model-fitting
asked 21 hours ago
meller92meller92
7310
Have you tried?
– Mstaino
17 hours ago
add a comment |
0
0
0
I'm wondering if it's possible using scipy.optimize.curve_fit or any other different out-of-the-box method to fit some arbitrary defined fuction(model) like e.g.:
def model_smooth_ramp(x, x0, x1, a, b, s):
y = np.piecewise(x, [(x < x0), (x0 <= x) * (x < x1), (x >= x1)], [0, lambda x: (x - x0) *(1/(x1-x0)), 1])
return a * smooth(y, window_len=s) + b
Where:
def smooth(x, window_len=6, window='flat'):
if x.ndim != 1:
raise ValueError("smooth only accepts 1 dimension arrays.")
if x.size < window_len:
raise ValueError("Input vector needs to be bigger than window size.")
if window_len < 3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError("Window is one of: 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
s = np.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]]
if window == 'flat': # moving average
w = np.ones(window_len, 'd')
else:
w = eval('np.' + window + '(window_len)')
y = np.convolve(w / w.sum(), s, mode='valid')
return y[(int(window_len / 2 - 1)):-(int(window_len / 2))]
python scikit-learn scipy data-fitting model-fitting
asked 21 hours ago
meller92meller92
7310
I'm wondering if it's possible using scipy.optimize.curve_fit or any other different out-of-the-box method to fit some arbitrary defined fuction(model) like e.g.:
def model_smooth_ramp(x, x0, x1, a, b, s):
y = np.piecewise(x, [(x < x0), (x0 <= x) * (x < x1), (x >= x1)], [0, lambda x: (x - x0) *(1/(x1-x0)), 1])
return a * smooth(y, window_len=s) + b
Where:
def smooth(x, window_len=6, window='flat'):
if x.ndim != 1:
raise ValueError("smooth only accepts 1 dimension arrays.")
if x.size < window_len:
raise ValueError("Input vector needs to be bigger than window size.")
if window_len < 3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError("Window is one of: 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
s = np.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]]
if window == 'flat': # moving average
w = np.ones(window_len, 'd')
else:
w = eval('np.' + window + '(window_len)')
y = np.convolve(w / w.sum(), s, mode='valid')
return y[(int(window_len / 2 - 1)):-(int(window_len / 2))]
python scikit-learn scipy data-fitting model-fitting
python scikit-learn scipy data-fitting model-fitting
asked 21 hours ago
meller92meller92
7310
asked 21 hours ago
meller92meller92
7310
asked 21 hours ago
meller92meller92
7310
asked 21 hours ago
meller92meller92
7310
asked 21 hours ago
meller92meller92
7310
7310
Have you tried?
– Mstaino
17 hours ago
add a comment |
Have you tried?
– Mstaino
17 hours ago
Have you tried?
– Mstaino
17 hours ago
Have you tried?
– Mstaino
17 hours ago
add a comment |
0
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Have you tried?
– Mstaino
17 hours ago