Mailman 3 February 2011 - Edu-sig

continued fractions (again)
by kirby urner 19 Feb '11

19 Feb '11

""" continued fractions >>> phi 1.6180371352785146 >>> pi 3.1415189855952756 """ phi =\ (1 + 1/ (1 + 1/ (1 + 1/ (1 + 1/ (1 + 1/ (1 + 1/ (1 + 1/ (1 + 1/ (1 + 1/ (1 + 1/ (1 + 1/ (1+ 1/ (1 + 1))))))))))))) pi =3 + \ (1**2/ (6 + 3**2/ (6 + 5**2/ (6 + 7**2/ (6 + 9**2/ (6 + 11**2/ (6 + 13**2/ (6 + 15**2/ (6 + 17**2/ (6 + 19**2/ (6 + 21**2/ (6 + 23**2/ (6 + 25**2/ (6+ 27**2/ 6)))))))))))))) def _test( ): import doctest doctest.testmod( ) if __name__ == "__main__": _test( )

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Re: [Edu-sig] Some interactive Python tutorials on basic stats, possibly useful for teaching
by John Posner 18 Feb '11

18 Feb '11

Hi Rajeev, > The tutorials are: > - interactive_mean_std_normal_distribution.py > - interactive_one_sample_t_test.py > - interactive_two_sample_t_test.py > - interactive_correlation_plot.py Very nice! I taught introductory stat in night school at Boston University -- it was a "one chapter ahead of the students" kind of gig. This kind of tool would have extremely valuable for helping students to develop an intuition for statistical concepts. Working with the "correlation" program (Python 2.6, Win/XP SP3 box), I found that I needed to insert the statement pylab.draw() at the end of the clear_the_figure_and_empty_points_list() function. Again, many thanks! -John

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Some interactive Python tutorials on basic stats, possibly useful for teaching
by Rajeev Raizada 17 Feb '11

17 Feb '11

Dear Python Edu-sig folks, Those of you whose duties include teaching basic stats might be interested in these interactive tutorial files, designed to illustrate basic concepts. Running the code opens up an interactive figure window. When you click on a figure to add new points, the statistical tests shown in the figure change accordingly. http://www.dartmouth.edu/~raj/intro-stats.html Here is a YouTube video showing the interactive demos in action: http://www.youtube.com/watch?v=nCv_MhaeFo8 The code has lots of comments in it, which attempt to explain the concepts as explicitly as possible. No prior knowledge of Python or statistics is assumed. These programs require the SciPy and Matplotlib modules. The tutorials are: - interactive_mean_std_normal_distribution.py - interactive_one_sample_t_test.py - interactive_two_sample_t_test.py - interactive_correlation_plot.py The same webpage also contains Matlab versions of the scripts. Please feel more than free to use any of the code for teaching, if you find it useful. Yours, Rajeev Raizada Research Assistant Professor Neukom Institute for Computational Science Dartmouth College HB 6255 Hanover NH 03755 Tel: 603 646 0175 E.mail: rajeev.raizada(a)dartmouth.edu WWW: http://www.dartmouth.edu/~raj

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Re: [Edu-sig] back on topic: apropos Go
by Mark Engelberg 12 Feb '11

12 Feb '11

Getting back on topic, there's a good Python book about this sort of "data mining" approach to AI with lots of great projects that are perfect for advanced students: http://www.amazon.com/Programming-Collective-Intelligence-Building-Applicat…

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off-topic: teachable Moore's Law moment coming
by Andy Judkis 12 Feb '11

12 Feb '11

This isn't Python-related, but things have been quiet here lately and I figure we can spare the bandwidth. . . I'm really excited about the upcoming Jeopardy! shows next week (Mon-Wed) with champion human players facing off against IBM's Watson. It's been a great opportunity to get students to think about what Moore's law means, and how computer technology is likely to affect their lives over the next few decades. Obviously the IBM hype machine is in overdrive, but I think they've earned the right. In my class, we were just discussing how chips are not getting faster these days because of thermal problems, but are still getting more powerful in their ability to do parallel computation, and what that means. IBM has a very good not-too-technical video on their site about how this applies to their system http://www-943.ibm.com/innovation/us/watson/what-is-watson/a-system-designe… There's also an episode of PBS's Nova about Watson that aired earlier this week, available on-line: http://www.pbs.org/wgbh/nova/tech/smartest-machine-on-earth.html I know that my kids are always surprised and excited to see stuff that we do in class have some relevance in the real world, and this is the best alignment I've ever encountered. Thanks, Andy

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Fwd: off-topic: teachable Moore's Law moment coming
by kirby urner 12 Feb '11

12 Feb '11

---------- Forwarded message ---------- From: kirby urner <kirby.urner(a)gmail.com> Date: Fri, Feb 11, 2011 at 10:53 PM Subject: Re: [Edu-sig] off-topic: teachable Moore's Law moment coming To: Andy Judkis <ajudkis(a)verizon.net> I've been watching these videos hand bringing Watson up in conversation. This wasn't much talked about on other lists I'm following, so from my point of view, thanks for making edu-sig your geocache. Kirby On Fri, Feb 11, 2011 at 3:56 PM, Andy Judkis <ajudkis(a)verizon.net> wrote: > This isn't Python-related, but things have been quiet here lately and I > figure we can spare the bandwidth. . . > > I'm really excited about the upcoming Jeopardy! shows next week (Mon-Wed) > with champion human players facing off against IBM's Watson. It's been a > great opportunity to get students to think about what Moore's law means, and > how computer technology is likely to affect their lives over the next few > decades. Obviously the IBM hype machine is in overdrive, but I think they've > earned the right. > > In my class, we were just discussing how chips are not getting faster these > days because of thermal problems, but are still getting more powerful in > their ability to do parallel computation, and what that means. IBM has a > very good not-too-technical video on their site about how this applies to > their system > http://www-943.ibm.com/innovation/us/watson/what-is-watson/a-system-designe… > > There's also an episode of PBS's Nova about Watson that aired earlier this > week, available on-line: > http://www.pbs.org/wgbh/nova/tech/smartest-machine-on-earth.html > > I know that my kids are always surprised and excited to see stuff that we > do in class have some relevance in the real world, and this is the best > alignment I've ever encountered. > > Thanks, > Andy > _______________________________________________ > Edu-sig mailing list > Edu-sig(a)python.org > http://mail.python.org/mailman/listinfo/edu-sig >

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polystuff.py
by kirby urner 11 Feb '11

11 Feb '11

""" polystuff.py (Python 3.x) For a curriculum segment when we're not yet wanting to build user-defined classes. showpoly -- return an evaluable string with x the variable evalpoly -- return a numeric result given an evaluable string and x's value multipoly -- return the product of two polynomials as a tuple of tuples """ from operator import itemgetter def showpoly(A): """ Return a close-to-classic polynomial as an evaluable Python string literal, given a sequence of tuples for input (see multipoly below). Some prettifying. >>> p1 = ((4,2),(-4,1),(-7,0)) >>> showpoly(p1) '4 * x**2 - 4 * x**1 - 7' """ terms = [] for coeff, exp in sorted(A, key=itemgetter(1), reverse=True): terms.append( "{sign} {coeff}{var}".format( sign = "+" if coeff > 0 else "-", coeff = abs(coeff), var = ' * x**{}'.format(exp) if exp>0 else "" )) ans = " ".join(terms) if ans[0] == "+": # prettify ans = ans[1:].strip() return ans def evalpoly(strpoly, x): """ Evaluates a polynomial string literal for a given x value >>> p1 = ((4,2),(-4,1),(-7,0)) >>> thestring = showpoly(p1) >>> thestring '4 * x**2 - 4 * x**1 - 7' >>> evalpoly(thestring, 10) 353 """ return eval(strpoly, {"x":x}) def multipoly(A, B): """ Multiply two polynomials, represented as sequences of tuples e.g.: 3*x**3 + 2*x**2 - x - 1 would be ((3,3),(2,2),(-1,1),(-1,0)) with internal pairs in any order (term order is arbitrary) >>> p1 = ((1,1),(-1,0)) >>> p2 = ((1,1),(1,0)) >>> multipoly(p1,p2) [(-1, 0), (1, 2)] >>> p1 = ((4,2),(-4,1),(-7,0)) >>> p2 = ((1,1),(3,0)) >>> multipoly(p1,p2) [(-21, 0), (-19, 1), (8, 2), (4, 3)] """ results = [] termsAB = [] # Cartesian product of terms in poly A and poly B # multiply coefficients, add exponents for termA in A: for termB in B: termsAB.append(( termA[0] * termB[0], # coefficients termA[1] + termB[1] # exponents )) # find maximum exponent degree = max([term[1] for term in termsAB]) # collect like terms (same exponent) for exp in range(degree+1): # from 0 to degree inclusive newterm = ( sum([term[0] for term in termsAB if term[1] == exp]), exp) if newterm[0] != 0: results.append(newterm) return results def _test( ): import doctest doctest.testmod() if __name__ == '__main__': _test()

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edu-sig @ PyCon
by Vern Ceder 10 Feb '11

10 Feb '11

Hi everyone, I'm just putting out the traditional "what to we want to do at PyCon?" feeler... They don't have the open space pages quite ready to go, but we can use the list to get started. In the past, we've gotten dinner one evening and then found an open space room to chat for a while... any thoughts? Cheers, Vern -- Vern Ceder vceder(a)gmail.com, vceder(a)dogsinmotion.com The Quick Python Book, 2nd Ed - http://bit.ly/bRsWDW

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