Path: csiph.com!newsfeed.hal-mli.net!feeder3.hal-mli.net!newsfeed.hal-mli.net!feeder1.hal-mli.net!newsfeed.xs4all.nl!newsfeed4.news.xs4all.nl!xs4all!post.news.xs4all.nl!not-for-mail Return-Path: X-Original-To: python-list@python.org Delivered-To: python-list@mail.python.org X-Spam-Status: OK 0.004 X-Spam-Evidence: '*H*': 0.99; '*S*': 0.00; 'exercise': 0.04; 'error:': 0.07; 'problem:': 0.07; 'skip:` 10': 0.07; '===': 0.09; 'subject:method': 0.09; 'valueerror:': 0.09; 'way:': 0.09; 'subject:How': 0.10; 'def': 0.12; 'array.': 0.16; 'numpy': 0.16; 'operands': 0.16; 'skip:" 70': 0.16; 'url:html)': 0.16; 'trying': 0.19; 'question': 0.24; 'this:': 0.26; 'array': 0.29; 'specified': 0.30; 'message-id:@mail.gmail.com': 0.30; 'gives': 0.31; 'getting': 0.31; 'url:wiki': 0.31; '"",': 0.31; '50,': 0.31; 'probability': 0.31; 'url:wikipedia': 0.31; 'file': 0.32; 'class': 0.32; 'supposed': 0.32; '(most': 0.33; 'skip:_ 10': 0.34; 'could': 0.34; '(2)': 0.35; 'but': 0.35; 'received:google.com': 0.35; 'method': 0.36; 'url:org': 0.36; 'implement': 0.38; 'skip:[ 10': 0.38; 'to:addr:python-list': 0.38; 'that,': 0.38; 'recent': 0.39; 'to:addr:python.org': 0.39; 'called': 0.40; 'how': 0.40; 'skip:n 10': 0.64; 'choose': 0.64; 'here': 0.66; 'broadcast': 0.68; 'from:charset:iso-8859-9': 0.84; 'x):': 0.84 DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:date:message-id:subject:from:to:content-type; bh=WVibQZ5IqR4oX7GaMLGZseUQ5ZI91FXQclPcFTeuYJs=; b=xQGO38ChELbn1W6DJArSsyPiQNWWFQoYNQV9kpI1+Smc/qPv8wB/i3TZmvrwgPXZKb LJof2mBoZSlNg5sJzq+1YMm4e7n/RE1yRB91TjfMHpa357WCUN5mTyQB2XOITBf2pODT h6k6kkxBqK9wbKmM/5qrrk5AGMRJsj7KFaFZZQOa8o7UnyifdZwbeoeCNmJehlqWHTHh KzRbLvncJWkmgWtxfbkLw5Q0vUF/kITsYvc5omhuUYe4nu1L8YCQYLd3kdcl+98WgToE /wiyhVwKiy32pmDfSfnX5eQmHmIhzbyoj+fKAIOgw9lA4r9Y0ZEh90Ev3HY4YKqLPiAx x7UQ== MIME-Version: 1.0 X-Received: by 10.204.98.68 with SMTP id p4mr47970bkn.147.1384473527941; Thu, 14 Nov 2013 15:58:47 -0800 (PST) Date: Fri, 15 Nov 2013 01:58:47 +0200 Subject: How to np.vectorize __call__ method From: =?ISO-8859-9?Q?Ya=FEar_Arabac=FD?= To: python-list@python.org Content-Type: text/plain; charset=ISO-8859-1 X-BeenThere: python-list@python.org X-Mailman-Version: 2.1.15 Precedence: list List-Id: General discussion list for the Python programming language List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Newsgroups: comp.lang.python Message-ID: Lines: 69 NNTP-Posting-Host: 2001:888:2000:d::a6 X-Trace: 1384473861 news.xs4all.nl 15963 [2001:888:2000:d::a6]:35811 X-Complaints-To: abuse@xs4all.nl Xref: csiph.com comp.lang.python:59492 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 "", line 1, in 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/