For me
expected_profit = integrate(function_profit(s_theta, s_p, s_p_bar, s_eta)*\
diff(function_cdf_theta(s_theta,
s_gamma), s_theta), (s_theta, 0, 1))
gives
ValueError: expected an expression convertible to a polynomial in
Polynomial ring in _x0, _x1 over
ZZ(s_gamma,s_p**s_eta)[_A0,_A1,_A2,_A3,_A4,_A5,_A6,_A7,_A8,_A9,_A10,_A11,_A12,_A13,_A14,_B0]
with lex order, got _x0*_x1**9*s_p**(3*s_eta)*(_x1*s_p**s_eta + 1) +
_x1*s_p**s_eta*(-_x0*_x1**6*s_gamma*s_p**(2*s_eta)*(_A1*_x1**3 +
3*_A11*_x0**2*_x1 + _A13*_x1 + _A14 + 2*_A2*_x0*_x1**2 + 2*_A4*_x0 +
3*_A5*_x0**2 + 2*_A7*_x0*_x1 + 4*_A8*_x0**3 + _A9*_x1**2) -
_x1**3*s_p**s_eta*(_B0*_x1**5*s_p**(2*s_eta) + _x1**4*s_p**s_eta*(_A0
+ 3*_A1*_x0*_x1**2 + _A11*_x0**3 + 4*_A12*_x1**3 + _A13*_x0 +
2*_A2*_x0**2*_x1 + 3*_A3*_x1**2 + 2*_A6*_x1 + _A7*_x0**2 +
2*_A9*_x0*_x1) + _x1**3*s_p**s_eta*(-2*_A0*_x1 - 2*_A1*_x0*_x1**3 -
2*_A10 - 2*_A11*_x0**3*_x1 - 2*_A12*_x1**4 - 2*_A13*_x0*_x1 -
2*_A14*_x0 - 2*_A2*_x0**2*_x1**2 - 2*_A3*_x1**3 - 2*_A4*_x0**2 -
2*_A5*_x0**3 - 2*_A6*_x1**2 - 2*_A7*_x0**2*_x1 - 2*_A8*_x0**4 -
2*_A9*_x0*_x1**2)))
This is in the git master.
(by the way, I think you dropped off list).
Aaron Meurer
On Thu, Jan 16, 2014 at 10:47 AM, eTna <[email protected]> wrote:
> Thanks Aaron for taking the time to answer and sorry not to have followed up
> faster. I'm not sure I get you right. In fact, as exposed below, I write
> expected profit in two ways which seem to be pretty much the same to me..
> one works for all values of gamma, not the other. So I'm not sure what's the
> difference.
>
> # Profit function and cdf
> s_theta, s_p, s_p_bar, s_eta, s_gamma = symbols('s_theta s_p s_p_bar s_eta
> s_gamma', positive = True)
>
> class function_profit(Function):
> nargs = 4
>
> @classmethod
> def eval(cls, s_theta, s_p, s_p_bar, s_eta):
> return Piecewise((0.0, s_p > s_p_bar), ((s_p - 0.1)*(s_theta +
> s_p**(-s_eta)), True))
>
>
> class function_cdf_theta(Function):
> nargs = 2
>
> @classmethod
> def eval(cls, s_theta, s_gamma):
> return Piecewise((0.0, s_theta < 0.0), (1.0, s_theta > 1.0),
> (s_theta**s_gamma, True))
>
> # Expected profit written in a way that appears to work fine
>
> expected_profit = integrate(function_profit(s_theta, s_p, s_p_bar, s_eta)*\
> diff(function_cdf_theta(s_theta, s_gamma),
> s_theta), (s_theta, 0, 1))
>
> def function_expected_profit(p, p_bar, eta, gamma):
> return expected_profit.subs([(s_p, p), (s_p_bar, p_bar), (s_eta, eta),
> (s_gamma, gamma)])
>
> p, p_bar, eta, gamma = 0.2, 1.0, 2.0, 0.5
> print function_expected_profit(p, p_bar, eta, gamma)
> # 2.53333333333333
>
> # Expected profit as I had first written it (works with gamma is 1 but not
> 0.5 etc.)
>
> class function_exp_profit(Function):
> # problem when gamma is not 1... why, should not be different from
> function_expected_profit
> nargs = 4
>
> @classmethod
> def eval(cls, s_p, s_p_bar, s_eta, s_gamma):
> return integrate(function_profit(s_theta, s_p, s_p_bar, s_eta)*\
> diff(function_cdf_theta(s_theta, s_gamma), s_theta),
> (s_theta, 0, 1))
>
> p, p_bar, eta, gamma = 0.2, 1.0, 2.0, 0.5
> print function_exp_profit(p, p_bar, eta, gamma)
> # ValueError: Non-suitable parameters.
>
>
>
> On Monday, December 23, 2013 5:29:20 AM UTC+1, Aaron Meurer wrote:
>>
>> The problem is with Integral(Piecewise((0, Or(s_theta < 0, s_theta >
>> 1)), (0.5*s_theta**(-0.5)*(0.1*s_theta + 2.5), True)), (s_theta, 0,
>> 1)).
>>
>> Aaron Meurer
>>
>> On Sun, Dec 22, 2013 at 10:29 PM, Aaron Meurer <[email protected]> wrote:
>> > This looks like a bug.
>> >
>> > Aaron Meurer
>> >
>> > On Thu, Dec 19, 2013 at 5:16 PM, eTna <[email protected]> wrote:
>> >> Hi,
>> >>
>> >> Trying to write something with sympy for the first time... I'm stuck
>> >> with
>> >> some error and not to sure which way to look. The code is fairly short:
>> >> I
>> >> define a profit function, a cdf, and would like to compute the expected
>> >> profit for a range of prices, depending on some parameter's value:
>> >>
>> >> s_theta, s_p, s_p_bar, s_eta, s_gamma = symbols('s_theta s_p s_p_bar
>> >> s_eta
>> >> s_gamma')
>> >>
>> >> class function_profit(Function):
>> >> nargs = 4
>> >>
>> >> @classmethod
>> >> def eval(cls, s_theta, s_p, s_p_bar, s_eta):
>> >> return Piecewise((0, s_p > s_p_bar), ((s_p - 0.1)*(s_theta +
>> >> s_p**(-s_eta)), True))
>> >>
>> >> class function_cdf_theta(Function):
>> >> nargs = 2
>> >>
>> >> @classmethod
>> >> def eval(cls, s_theta, s_gamma):
>> >> return Piecewise((0, s_theta < 0), (1, s_theta > 1),
>> >> (s_theta**s_gamma,
>> >> True))
>> >>
>> >> class function_expected_profit(Function):
>> >> nargs = 4
>> >>
>> >> @classmethod
>> >> def eval(cls, s_p, s_p_bar, s_eta, s_gamma):
>> >> return integrate(function_profit(s_theta, s_p, s_p_bar, s_eta)*\
>> >> diff(function_cdf_theta(s_theta, s_gamma),
>> >> s_theta),
>> >> (s_theta, 0, 1))
>> >>
>> >> for gamma in [0.5, 0.7, 1.0]:
>> >> x_axis = np.linspace(0.1, 2.0, int(round(((2.0-0.1)/0.1)))+1)
>> >> ls_profits = [function_expected_profit(x, 2.0, 2.0, gamma) for x in
>> >> x_axis]
>> >> plt.plot(x_axis, ls_profits, label = 'gamma = %s' %gamma)
>> >> legend = plt.legend(loc='upper right')
>> >> plt.show()
>> >>
>> >> The profit and cdf functions seem to work fine, the differentiation of
>> >> the
>> >> cdf as well... but I can compute the expected profit only for gamma =
>> >> 1.
>> >> Trying to compute the expected profit with gamma = 0.5, I get
>> >> "ValueError:
>> >> Non-suitable parameters.".
>> >>
>> >> Many thanks for your time and sorry about the noobish question,
>> >>
>> >> Etienne
>> >>
>> >> --
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