I am cross-posting from: http://stackoverflow.com/q/19990863/886669
I am following, [quant-econ](http://quant-econ.net/numpy.html) tutorial. I am trying the exercise where I am supposed to implement a [Empirical Cumulative Probability Funcion](http://en.wikipedia.org/wiki/Empirical_distribution_function) using vectorized numpy methods. Here is the **correct** solution to problem: class ecdf: def __init__(self, observations): self.observations = np.asarray(observations) def __call__(self, x): return np.mean(self.observations <= x) def plot(self, a=None, b=None): # === choose reasonable interval if [a, b] not specified === # if not a: a = self.observations.min() - self.observations.std() if not b: b = self.observations.max() + self.observations.std() # === generate plot === # x_vals = np.linspace(a, b, num=100) f = np.vectorize(self.__call__) plt.plot(x_vals, f(x_vals)) plt.show() But I am **trying** to do it this way: class ecdf(object): def __init__(self, observations): self.observations = np.asarray(observations) self.__call__ = np.vectorize(self.__call__) def __call__(self, x): return np.mean(self.observations <= x) So that, `__call__` method is vectorized and instance can be called with an array and it returns an array of cumulative probabilities for that array. However, when I try it like this: p = ecdf(uniform(0,1,500)) p([0.2, 0.3]) I am getting this error: Traceback (most recent call last): File "<ipython-input-34-6a77f18aa54e>", line 1, in <module> p([0.2, 0.3]) File "D:/Users/y_arabaci-ug/Desktop/quant-econ/programs/numpy_exercises.py", line 50, in __call__ return np.mean(self.observations <= x) ValueError: operands could not be broadcast together with shapes (500) (2) My question is, how come author could vectorize `self.__call__` and it works, while my method gives an error? -- http://ysar.net/ -- https://mail.python.org/mailman/listinfo/python-list
