def __call__(self):
if self.model!='nugget':
# what are 3 and 10? should these be parametrizable?
params = fit_function(self.x, self.y, self.model, 3, 10)
else:
# what are 1 and 1? should these be parametrizable?
params = fit_function(self.x, self.y, self.model, 1, 1)
return self.model(self.x, params)
def __call__(self):
if self.model_text != 'nugget':
# what are 3 and 10? should these be parametrizable?
params = fit_function(self.x, self.y, self.model, 3, 10)
y = self.model(self.x, params)
else:
# what are 1 and 1? should these be parametrizable?
params = fit_function(self.x, self.y, self.model, 1, 1)
y = [self.model(self.x, params)] * len(self.x)
return y, params, self.text
def __call__(self):
if self.model_text!='nugget':
# what are 3 and 10? should these be parametrizable?
params = fit_function(self.x, self.y, self.model, 3, 10)
y = self.model(self.x, params)
else:
# what are 1 and 1? should these be parametrizable?
params = fit_function(self.x, self.y, self.model, 1, 1)
y = [self.model(self.x, params)] * len(self.x)
return y, params, self.text
def test_exponential(self):
"""test exponential approximation"""
# defining our fitting function
def f(x,a):
return exp(a[0]+x*a[1])
exp_params = [2, 10]
x = arange(-1,1,.01)
y = f(x, exp_params)
y_noise = y + rand(len(y))
params = fit_function(x, y_noise, f, 2, 5)
self.assertFloatEqual(params, exp_params , .5)
def test_constant(self):
"""test constant approximation"""
# defining our fitting function
def f(x,a):
return a[0]
exp_params = [2]
x = arange(-1,1,.01)
y = f(x, exp_params)
y_noise = y + rand(len(x))
params = fit_function(x, y_noise, f, 1, 5)
self.assertFloatEqual(params, exp_params , .5)
def test_exponential(self):
"""test exponential approximation"""
# defining our fitting function
def f(x,a):
return exp(a[0]+x*a[1])
exp_params = [2, 10]
x = arange(-1,1,.01)
y = f(x, exp_params)
y_noise = y + rand(len(y))
params = fit_function(x, y_noise, f, 2, 5)
self.assertFloatEqual(params, exp_params , .5)
def test_constant(self):
"""test constant approximation"""
# defining our fitting function
def f(x,a):
return a[0]
exp_params = [2]
x = arange(-1,1,.01)
y = f(x, exp_params)
y_noise = y + rand(len(x))
params = fit_function(x, y_noise, f, 1, 5)
self.assertFloatEqual(params, exp_params , .5)
