kmike / pymorphy / issues / #59 - Command "python setup.py ... | command python setup py egg_info failed with error code 1Image Source:
command python setup py egg_info failed with error code 1
Python is an interpreted, interactive, acquisitive programming language. It is about compared to Tcl, Perl, Scheme or Java.
["630.5"]
xgboost - Command "python setup.py egg_info" failed with error ... | command python setup py egg_info failed with error code 1Image Source:
Python combines arresting ability with actual bright syntax. It has modules, classes, exceptions, actual aerial akin activating abstracts types, and activating typing. There are interfaces to abounding arrangement calls and libraries, as able-bodied as to assorted windowing systems (X11, Motif, Tk, Mac, MFC, wxWidgets). New congenital modules are calmly accounting in C or C . Python is additionally accessible as an addendum accent for applications that charge a programmable interface.
The Python accomplishing is portable: it runs on abounding brands of UNIX, on Windows, OS/2, Mac, Amiga, and abounding added platforms.
Some of Python's notable features:
We will not altercate the specifics of Python actuality but instead accredit the clairvoyant to the Python website. Additionally see Dive Into Python - Python from amateur to pro
lpsolve is callable from Python via an addendum or module. As such, it looks like lpsolve is absolutely chip with Python. Matrices can anon be transferred amid Python and lpsolve in both directions. The complete interface is accounting in C so it has best performance. The accomplished lpsolve API is implemented with some extra's specific for Python (especially for cast support). So you acquire abounding ascendancy to the complete lpsolve functionality via the lpsolve Python driver. If you acquisition that this involves too abundant assignment to break an lp archetypal afresh you can additionally assignment via higher-level calligraphy files that can accomplish things a lot easier. See added in this article.
Python is alluringly ill-fitted to handle beeline programming problems. These are problems in which you acquire a quantity, depending linearly on several variables, that you appetite to aerate or abbreviate accountable to several constraints that are bidding as beeline inequalities in the aforementioned variables.�If the cardinal of variables and the cardinal of constraints are small, afresh there are abundant algebraic techniques for analytic a beeline programming problem. Indeed these techniques are about accomplished in aerial academy or university akin courses in apprenticed mathematics.�But sometimes these numbers are high, or alike if low, the constants in the beeline inequalities or the commodity announcement for the abundance to be optimised may be numerically complicated in which case a software amalgamation like Python is adapted to aftereffect a solution.
To accomplish this possible, a disciplinarian affairs is needed: lpsolve55.pyd/lpsolve55.so. This disciplinarian charge be put in a agenda accustomed to Python and Python can alarm the lpsolve solver.
This disciplinarian calls lpsolve via the lpsolve aggregate library (lpsolve55.dll beneath Windows and liblpsolve55.so beneath Unix/Linux) (archive lp_solve_5.5.2.0_dev.zip/lp_solve_5.5.2.0_dev.tar.gz). This has the advantage that the lpsolve disciplinarian doesn't acquire to be recompiled aback an amend of lpsolve is provided. The aggregate library charge be about in the Windows path.
So agenda the aberration amid the Python lpsolve disciplinarian that is alleged lpsolve and the lpsolve library that accouterments the API that is alleged lpsolve55.
There are additionally some Python calligraphy files (.py) as a quick start.
See Install the lpsolve disciplinarian for the accession of these files.
To analysis if aggregate is installed correctly, admission the afterward in the Python command window.
If it gives the following, afresh aggregate is ok:
If you get the following:
Or (Unix/Linux):
Then Python can acquisition the lpsolve disciplinarian program, but the disciplinarian affairs cannot acquisition the lpsolve library that contains the lpsolve implementation. This library is alleged lpsolve55.dll beneath Windows and liblpsolve55.so beneath Unix/Linux.Under Windows, the lpsolve55.dll book charge be in a agenda that in the PATH ambiance variable. This aisle can be credible via the afterward command in Python: !PATHIt is accustomed to abode this in the WINDOWSsystem32 folder.
Under Unix/Linux, the liblpsolve55.so aggregate library charge be either in the directories /lib or /usr/lib or in a agenda authentic by the LD_LIBRARY_PATH ambiance variable.
In the afterward text, >>> afore the Python commands is the Python prompt. Alone the altercation afterwards >>> charge be entered.
To alarm an lpsolve function, the afterward syntax charge be used:
The acknowledgment ethics are addition and depend on the action called. functionname charge consistently be amid amid distinct quotes to accomplish it alphanumerical and it is case sensitive. The cardinal and blazon of arguments depend on the action called. Some functions alike acquire a capricious cardinal of arguments and a altered behaviour occurs depending on the blazon of the argument. functionname can be (almost) any of the lpsolve API routines (see lp_solve API reference) added some added Python specific functions. Best of the lpsolve API routines use or acknowledgment an lprec structure. To accomplish things added able-bodied in Python, this anatomy is replaced by a handle or the archetypal name. The lprec structures are maintained internally by the lpsolve driver. The handle is an incrementing cardinal starting from 0. Starting from disciplinarian adaptation 5.5.0.2, it is additionally accessible to use the archetypal name instead of the handle. This can of beforehand alone be done if a name is accustomed to the model. This is done via lpsolve accustomed set_lp_name or by allegorical the archetypal name in accustomed read_lp. See Appliance archetypal name instead of handle.
Almost all callable functions can be activate in the lp_solve API reference. Some are absolutely as declared in the advertence guide, others acquire a hardly altered syntax to accomplish best use of the Python functionality. For archetype make_lp is acclimated identical as described. But get_variables is hardly different. In the API reference, this action has two arguments. The aboriginal the lp handle and the added the consistent variables and this arrangement charge already be dimensioned. Aback lpsolve is acclimated from Python, annihilation charge be dimensioned in advance. The lpsolve disciplinarian takes affliction of dimensioning all acknowledgment variables and they are consistently alternate as acknowledgment amount of the alarm to lpsolve. Never as altercation to the routine. This can be a distinct amount as for get_objective or a cast or agent as in get_variables. In this case, get_variables allotment a 4x1 cast (vector) with the aftereffect of the 4 variables of the lp model.
Note that you can get a acceptance of lpsolve, its arguments and the constants that it defines by entering the afterward in Python:
Also see Appliance cord constants for an alternative.
(Note that you can assassinate this archetype by entering command per command as credible beneath or by active calligraphy example1. This will assassinate example1.py.)
Note that there are commands that acknowledgment an answer. If they do and their aftereffect is not stored in a variable, afresh it is echoed on screen. If assigned to a capricious afresh the amount is not echoed. For example:
The aftereffect in afresh not credible on screen. But the accommodation of the capricious can be printed:
Or even:
Note that get_variables and get_constraints acknowledgment two results: The aftereffect agent and a status. If alone the agent is needed, afresh [0] can be acclimated as in the example. Or the aftereffect can be stored in an added variables:
Variable x will accommodate the aftereffect agent and ret the acknowledgment cachet of the call.
Note that if this is stored alone in one capricious that you get the following:
Don't balloon to chargeless the handle and its associated anamnesis aback you are done:
Note that there is addition way to admission the lpsolve library. It is added commodity oriented, but requires added typing:
This address will not be acclimated in this description because the lpsolve library is not absolutely commodity aggressive structured. But if you adopt it, there is annihilation that should stop you. The alone aberration is that you acquire to put lpsolve55. afore every lpsolve action and constant.
From disciplinarian adaptation 5.5.0.2, it is accessible to use the archetypal name instead of the handle. From the moment the archetypal has a name, you can use this name instead of the handle. This is best credible by an example. Aloft archetype would attending like this:
So everywhere a handle is needed, you can additionally use the archetypal name. You can alike mix the two methods. There is additionally a specific Python accustomed to get the handle from the archetypal name: get_handle.For example:
Don't balloon to chargeless the handle and its associated anamnesis aback you are done:
In the abutting allotment of this documentation, the handle is used. But if you name the model, the name could appropriately additionally be used.
lpsolve uses Python lists to represent matrices (and vectors). Agenda that lpsolve can alone assignment with complete numbers, not circuitous numbers. For example:
[0.24, 0, 11.31, 0] is a account blazon variable.
Most of the time, variables are acclimated to accommodate the data:
Where a1 is a capricious of blazon list. Sometimes a two-dimensional cast is used:
[1, 2, 3] is the aboriginal row and [4, 5, 6] is the added row.
The lpsolve disciplinarian sees all provided matrices as dispersed matrices. lpsolve uses dispersed matrices internally and abstracts can be provided dispersed via the ex routines. For archetype add_constraintex. The lpsolve disciplinarian consistently uses the ex routines to accommodate the abstracts to lpsolve. Alike if you alarm from Python the accustomed names that would crave a close cast (for archetype add_constraint), the lpsolve disciplinarian will consistently alarm the dispersed adaptation of the accustomed (for archetype add_constraintex). This after-effects in the best assuming behaviour. Matrices with too few or too abundant elements gives an 'invalid vector.' error.
An important final note. Several lp_solve API routines acquire a agent breadth the aboriginal aspect (element 0) is not used. Added lp_solve API calls do use the aboriginal element. In the Python interface, there is never an bare aspect in the matrices. So if the lp_solve API specifies that the aboriginal aspect is not used, afresh this aspect is not in the Python matrix.
["1185.34"]
Python3 pip install gives “Command ”python setup.py egg_info ... | command python setup py egg_info failed with error code 1Image Source:
Because Python has the account achievability to represent vectors, all lpsolve API routines that charge a cavalcade or row cardinal to get/set advice for that column/row are continued in the lpsolve Python disciplinarian to additionally assignment with vectors. For archetype set_int in the API can alone set the accumulation cachet for one column. If the cachet for several accumulation variables charge be set, afresh set_int charge be alleged assorted times. The lpsolve Python disciplinarian about additionally allows allegorical a agent to set the accumulation cachet of all variables at once. The API alarm is: acknowledgment = lpsolve('set_int', lp, column, must_be_int). The cast adaptation of this alarm is: acknowledgment = lpsolve('set_int', lp, [must_be_int]). The API alarm to acknowledgment the accumulation cachet of a capricious is: acknowledgment = lpsolve('is_int', lp, column). The cast adaptation of this alarm is: [is_int] = lpsolve('is_int', lp)Also agenda the get_mat and set_mat routines. In Python these are continued to return/set the complete coercion matrix. See afterward example.
Above archetype can appropriately additionally be done as follows:(Note that you can assassinate this archetype by entering command per command as credible beneath or by active calligraphy example2. This will assassinate example2.py.)
Note the acceptance of Complete in set_upbo. This stands for 'infinity'. Acceptation an complete high bound. It is additionally accessible to use -Infinite to accurate bare infinity. This can for archetype be acclimated to actualize a chargeless variable. Complete is a connected authentic by the lpsolve library.
To appearance the abounding ability of the matrices, let's now do some cast calculations to analysis the solution. It works added on aloft example. Agenda that Python doesn't abutment cast calculations on lists. However, there are several afterwards bales that can do this. The account blazon variables charge be adapted to the cast types of these packages. Two accustomed accepted bales for this are Numeric and numpy. These are not installed by absence but can be calmly installed. See http://sourceforge.net/project/showfiles.php?group_id=1369&package_id=1351 and http://sourceforge.net/projects/numpy/files/. See http://numpy.scipy.org/ for a abrupt overview.
or
This affidavit works with numpy, but Numeric can be acclimated also.
We alike don't acquire to absolutely catechumen the lists to matrices:
dot allotment not a list, but an arrangement object. If the aftereffect is bare afresh in a list, afresh this can be calmly done:
So what we acquire done actuality is account the ethics of the constraints (RHS) by adding the coercion cast with the band-aid vector. Now booty a attending at the ethics of the constraints that lpsolve has found:
Exactly the aforementioned as the affected B vector, as expected.
Also the amount of the cold can be affected in a aforementioned way:
So what we acquire done actuality is account the amount of the cold by adding the cold agent with the band-aid vector. Now booty a attending at the amount of the cold that lpsolve has found:
Again absolutely the aforementioned as the affected obj value, as expected.
In the aloft breadth Best acceptance of matrices with lpsolve the amalgamation numpy was already mentioned. See http://numpy.scipy.org/ for a abrupt overview. This amalgamation is the almsman of the earlier and anachronistic amalgamation Numeric. Aback lp_solve is all about arrays and matrices, it is analytic that the lpsolve Python disciplinarian accepts numpy arrays. This is accessible from disciplinarian adaptation 5.5.0.9. Afore it was bare that numpy arrays were adapted to lists.For example:
Note that the numpy arrangement capricious c is anesthetized anon to lpsolve. Afore disciplinarian adaptation 5.5.0.9 this gave an absurdity aback lpsolve did not apperceive numpy arrays. They had to be adapted to lists:
That is ok for baby models, but for above arrays this gives an added anamnesis aerial aback c is now two times in memory. Already as the numpy arrangement and already as list.
Note that all alternate arrays from lpsolve are consistently lists.
Also agenda that the earlier amalgamation Numeric is not accurate by lpsolve. So it is not accessible to accommodate a Numeric arrangement to lpsolve. That will accord an error.
From disciplinarian adaptation 5.5.2.0 on, it is accessible to use cord constants everywhere an lp_solve connected is bare or returned. This is best credible by an example. In the aloft cipher we had:
Note the 3rd connected on set_verbose and the 4th on add_constraint. These are lp_solve constants. One can ascertain all the accessible constants in Python as is done in the Python disciplinarian and afresh use them in the calls, but that has several disadvantages. Aboriginal there stays the achievability to accommodate a connected that is not advised for that accurate call. Addition affair is that calls that acknowledgment a connected are still abiding it numerical.
Both issues can now be handled by cord constants. The aloft cipher can be done as afterward with cord constants:
This is not alone added readable, there is abundant bottom adventitious that mistakes are actuality made. The calling accustomed knows which constants are accessible and alone allows these. So alien constants or constants that are advised for added calls are not accepted. For example:
Note the aberration amid the two absurdity messages. The aboriginal says that the connected is not known, the added that the connected cannot be acclimated at that place.
Constants are case insensitive. Internally they are consistently translated to high case. Additionally aback alternate they will consistently be in high case.
The connected names are the ones as authentic in the affidavit of anniversary API routine. There are alone 3 exceptions, extensions actually. 'LE', 'GE' and 'EQ' in add_constraint and is_constr_type can additionally be '<', '<=', '>', '>=', '='. Aback alternate however, 'GE', 'LE', 'EQ' will be used.
Also in the cast adaptation of calls, cord constants are possible. For example:
Some constants can be a aggregate of assorted constants. For archetype set_scaling:
With the cord adaptation of constants this can be done as following:
| is the OR abettor acclimated to amalgamate assorted constants. There may optinally be spaces afore and afterwards the |.
Not all OR combinations are legal. For archetype in set_scaling, a best charge be fabricated amid SCALE_EXTREME, SCALE_RANGE, SCALE_MEAN, SCALE_GEOMETRIC or SCALE_CURTISREID. They may not be accumulated with anniversary other. This is additionally tested:
Everywhere constants charge be provided, numeric or cord ethics may be provided. The accustomed automatically interpretes them.
Returning constants is a altered story. The user charge let lp_solve apperceive how to acknowledgment it. Afterwards or as string. The absence is numerical:
To let lp_solve acknowledgment a connected as string, a alarm to a new action charge be made: return_constants
From now on, all alternate constants are alternate as string:
Also aback an arrangement of constants is returned, they are alternate as cord aback return_constants is set:
This for all routines until return_constants is afresh alleged with 0:
The (new) accepted ambience of return_constants is consistently alternate by the call. Alike aback set:
To get the amount afterwards ambience it, don't accommodate the added argument:
In the abutting allotment of this documentation, return_constants is the default, 0, so all constants are alternate afterwards and provided constants are additionally numerical. This to accumulate the affidavit as accordant as accessible with earlier versions. But don't let you authority that aback to use cord constants in your code.
Python can assassinate a arrangement of statements stored in diskfiles. Such files are alleged Python scripts and should acquire the book blazon of ".py" as the aftermost allotment of their filename (extension).
You can put Python commands in them and assassinate them at any time. The Python calligraphy is accomplished either via the command python script.py. The calligraphy files accommodate credible ascii data.
The lpsolve Python administration contains some archetype calligraphy to authenticate this.
Contains the commands as credible in the aboriginal archetype of this article.
Contains the commands as credible in the added archetype of this article.
["1862.4"]Image Source:
Contains the commands of a applied example. See added in this article.
Contains the commands of a applied example. See added in this article.
Contains the commands of a applied example. See added in this article.
Contains the commands of a applied example. See added in this article.
This calligraphy uses the API to actualize a higher-level action alleged lp_solve. This action accepts as arguments some matrices and options to actualize and break an lp model. Blazon help(lp_solve) or aloof lp_solve() to see its usage:
Example of usage. To actualize and break afterward lp-model:
The afterward command can be used:
This calligraphy is analog to the lp_solve calligraphy and additionally uses the API to actualize a higher-level action alleged lp_maker. This action accepts as arguments some matrices and options to actualize an lp model. Agenda that this scripts alone creates a archetypal and allotment a handle. Blazon help(lp_maker) or aloof lp_maker() to see its usage:
Example of usage. To actualize afterward lp-model:
The afterward command can be used:
To break the archetypal and get the solution:
Don't balloon to chargeless the handle and its associated anamnesis aback you are done:
Contains several examples to body and break lp models.
Contains several examples to body and break lp models. Additionally solves the lp_examples from the lp_solve distribution.
We shall allegorize the adjustment of beeline programming by agency of a simple example, giving a aggregate graphical/numerical solution, and afresh break both a hardly as able-bodied as a essentially added complicated problem.
Suppose a agriculturalist has 75 acreage on which to bulb two crops: aureate and barley. To aftermath these crops, it costs the agriculturalist (for seed, fertilizer, etc.) $120 per acre for the aureate and� $210 per acre for the barley.�The agriculturalist has $15000 accessible for expenses. But afterwards the harvest, the agriculturalist charge abundance the crops while apprehension favourable bazaar conditions. The agriculturalist has accumulator amplitude for 4000 bushels.�Each acre yields an boilerplate of 110 bushels of aureate or 30 bushels of barley.� If the net accumulation per bassinet of aureate (after all costs acquire been subtracted) is $1.30 and for barley is $2.00, how should the agriculturalist bulb the 75 acreage to aerate profit?
We activate by formulating the botheration mathematically.� Aboriginal we accurate the objective, that is the profit, and the constraints algebraically, afresh we blueprint them, and after we admission at the band-aid by graphical analysis and a accessory addition calculation.
Let x denote the cardinal of acreage allotted to aureate and y the cardinal of acreage allotted to barley. Afresh the announcement to be maximized, that is the profit, is clearly
P = (110)(1.30)x (30)(2.00)y = 143x 60y.
There are three coercion inequalities, authentic by the banned on expenses, accumulator and acreage. They are respectively:
120x 210y <= 15000110x 30y <= 4000x y <= 75
Strictly speaking there are two added coercion inequalities affected by the actuality that the agriculturalist cannot bulb a abrogating cardinal of acres, namely:
x >= 0,�y >= 0.
Next we blueprint the regions authentic by the constraints. The aftermost two say that we alone charge to accede the aboriginal division in the x-y plane. Here's a blueprint delineating the triangular arena in the aboriginal division bent by the aboriginal inequality.
Now let's put in the added two coercion inequalities.
The atramentous breadth is the band-aid amplitude that holds accurate solutions. This agency that any point in this breadth fulfils the constraints.
Now let's blanket on top of this account a ambit artifice of the cold action P.
The ambit accord a account of the cold function. All solutions that bisect with the atramentous breadth are accurate solutions, acceptation that this aftereffect additionally fulfils the set constraints. The added the ambit go to the right, the college the cold amount is. The optimal band-aid or best cold is a band that is still in the atramentous area, but with an as ample as accessible value.
It seems credible that the best amount of P will action on the akin ambit (that is, akin line) that passes through the acme of the polygon that lies abreast (22,53).It is the circle of x y = 75 and 110*x 30*y = 4000This is a bend point of the diagram. This is not a coincidence. The canker algorithm, which is acclimated by lpsolve, starts from a assumption that the optimal band-aid is such a bend point.In actuality we can compute the result:
The acreage that after-effects in the best accumulation is 21.875 for aureate and 53.125 for barley. In that case the accumulation is:
That is, $6315.625.
Note that these command are in calligraphy example3.py
Now, lp_solve comes into the account to break this beeline programming botheration added generally. Afterwards that we will use it to break two added complicated problems involving added variables and constraints.
For this example, we use the higher-level calligraphy lp_maker to body the archetypal and afresh some lp_solve API calls to retrieve the solution. Actuality is afresh the acceptance of lp_maker:
Now let's codify this archetypal with lp_solve:
Note that these command are in calligraphy example4.py
With the higher-level calligraphy lp_maker, we accommodate all abstracts to lp_solve. lp_solve allotment a handle (lp) to the created model. Afresh the API alarm 'solve' is acclimated to account the optimal band-aid of the model. The amount of the cold action is retrieved via the API alarm 'get_objective' and the ethics of the variables are retrieved via the API alarm 'get_variables'. At last, the archetypal is removed from anamnesis via a alarm to 'delete_lp'. Don't balloon this to chargeless all anamnesis allocated by lp_solve.
The band-aid is the aforementioned acknowledgment we acquired before.� Agenda that the non-negativity constraints are accounted around because variables are by absence non-negative in lp_solve.
Well, we could acquire done this botheration by duke (as credible in the introduction) because it is actual baby and it can be graphically presented.Now accept that the agriculturalist is ambidextrous with a third crop, say corn, and that the agnate abstracts is:
With three variables it is already a lot added difficult to appearance this archetypal graphically. Abacus added variables makes it alike absurd because we can't brainstorm anymore how to represent this. We alone acquire a applied compassionate of 3 dimensions, but above that it is all actual theoretical.
If we denote the cardinal of acreage allotted to blah by z, afresh the cold action becomes:
["817.71"]
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P = (110)(1.30)x (30)(2.00)y� (125)(1.56) = 143x 60y 195z
And the coercion inequalities are:
120x 210y 150.75z <= 15000110x 30y 125z <= 4000x y z <= 75x >= 0,�y >= 0, z >= 0
The botheration is credible with lp_solve as follows:
Note that these command are in calligraphy example5.py
So the agriculturalist should canal the aureate and bulb 56.5789 acreage of barley and 18.4211 acreage of corn.
There is no applied complete on the cardinal of variables and constraints that Python can handle. Certainly none that the about artless user will encounter.�Indeed, in abounding accurate applications of the address of beeline programming, one needs to accord with abounding variables and constraints.�The band-aid of such a botheration by duke is not feasible, and software like Python is acute to success.�For example, in the agriculture botheration with which we acquire been working, one could acquire added crops than two or three. Think agribusiness instead of ancestors farmer.�And one could acquire constraints that appear from added things beside expenses, accumulator and acreage limitations. For example:
Below is a arrangement of commands that solves absolutely such a problem. You should be able to admit the cold announcement and the constraints from the abstracts that is entered.� But as an aid, you ability acknowledgment the afterward questions:
Note that these command are in calligraphy example6.py
Note that we acquire acclimated in this conception the vlb and vub arguments of lp_maker. This to set lower and high apprenticed on variables. This could acquire been done via added constraints, but it is added performant to set apprenticed on variables. Additionally agenda that Above is acclimated for variables that acquire no high limit.
Note that admitting the complication of the problem, lp_solve solves it about instantaneously. It seems the agriculturalist should bet the acreage on crop cardinal 6.�We acerb advance you adapt the amount and/or the accumulator complete in the botheration and see what aftereffect that has on the answer.
Suppose we appetite to break the afterward beeline affairs appliance Python:
max 4x1 2x2 x3s. t. 2x1 x2 <= 1x1 2x3 <= 2x1 x2 x3 = 1x1 >= 0x1 <= 1x2 >= 0x2 <= 1x3 >= 0x3 <= 2
Convert the LP into Python architecture we get:
f = [4, 2, 1]A = [[2, 1, 0], [1, 0, 2], [1, 1, 1]]b = [1, 2, 1]
Note that constraints on distinct variables are not put in the coercion matrix. lp_solve can set apprenticed on alone variables and this is added performant than creating added constraints. These apprenticed are:
l = [ 0, 0, 0]u = [ 1, 1, 2]
Now lets admission this in Python:
Now break the beeline affairs appliance Python: Blazon the commands
What to do aback some of the variables are missing ?For example, accept there are no lower apprenticed on the variables. In this case ascertain l to be the abandoned set appliance the Python command:
This has the aforementioned aftereffect as before, because lp_solve has as absence lower apprenticed for variables 0.
But what if you appetite that variables may additionally become negative?Then you can use -Infinite as lower bounds:
Solve this and you get a altered result:
Note that everwhere breadth lp is acclimated as altercation that this can be a handle (lp) or the models name.
These routines are not allotment of the lpsolve API, but are added for backwards compatibility. Best of them abide in the lpsolve API with addition name.
The lpsolve Python disciplinarian is alleged lpsolve55.pyd (windows) and lpsolve55.so (Unix).This disciplinarian is an interface to the lpsolve55.dll (windows) and liblpsolve55.so (Unix) lpsolve aggregate library that contains the accomplishing of lp_solve. lpsolve55.dll/liblpsolve55.so is broadcast with the lp_solve amalgamation (archive lp_solve_5.5.2.0_dev.zip/lp_solve_5.5.2.0_dev.tar.gz). The lpsolve Python disciplinarian is aloof a adhesive amid Python and lp_solve to construe the input/output to/from Python and the lp_solve library.
The lpsolve Python disciplinarian is accounting in C. To abridge this cipher a C compiler is needed. Beneath Unix, this is the accustomed C compiler (cc/gcc) and beneath windows it is the Microsoft compiler from Beheld Studio .NET. or the mingw gcc compiler This compiler charge be alleged from Python. To accomplish the accumulation action easier, a Python calligraphy can be used: setup.py (setup64.py for 64 bit OS)
To be able to abridge this driver, Python charge be installed on the arrangement and callable from a carapace or command alert by aloof entering the command Python. If this is not the case, set the PATH ambiance capricious to the area breadth Python is installed. For archetype C:Python25
The lpsolve Python disciplinarian can use the Python numpy package. To be able to abridge the lpsolve driver, this numpy amalgamation should additionally be installed on the system. If the numpy amalgamation cannot be activate at accumulation time, the lpsolve Python disciplinarian will not be able to assignment with numpy. The accumulation calligraphy setup.py/setup64.py automatically detects if numpy is installed and uses it if found. This is done by analytic the numpy directory. This is done by the afterward python commands:
Under Unix, accomplish abiding you acquire abundant rights. Possibly you charge be logged in as a basis user.
Under Windows, the Microsoft beheld C compiler is bare by default.Unfortunately allegedly there is a analysis that the adaptation charge be absolutely the aforementioned as the one breadth Python itself is body with.However it is additionally accessible to abridge with the mingw gcc compiler. For this, actualize (or edit) the book C:Python25Libdistutilsdistutils.cfg:
The python binder can of beforehand be different.
Note that liblpsolve55.a may not abide in agenda ../../lpsolve55/bin/win32. Delete it or acting rename it such that lpsolve55.dll is used.
Now to abridge and install the package, aloof admission the afterward in a command prompt/shell:
or
and aggregate is done.
To actualize a administration package, enter:
Under Windows, use:
Under Linux, the distributable bales is alleged lpsolve55-5.5.0.6.linux-i686.tar.gzThe accommodation charge be broadcast to the adapted directory.
Under Windows, there is an installer that installs the Python lpsolve disciplinarian and the acknowledging scripts lp_maker.py and lp_solve.py. Aloof run lpsolve55-5.5.0.6.win32-py2.4.exe (this installer is in annal lp_solve_5.5_Python_exe.zip).
On added platforms, the lpsolve disciplinarian charge be aggregate as declared in the antecedent section: Abridge the lpsolve driver
Don't balloon that lpsolve55.dll (Windows) or liblpsolve55.so (Unix/Linux) is additionally bare for this (In annal lp_solve_5.5.2.0_dev.zip).
See additionally Appliance lpsolve from MATLAB, Appliance lpsolve from O-Matrix, Appliance lpsolve from Sysquake, Appliance lpsolve from Scilab, Appliance lpsolve from Octave, Appliance lpsolve from FreeMat, Appliance lpsolve from Euler, Appliance lpsolve from PHP, Appliance lpsolve from Sage, Appliance lpsolve from R, Appliance lpsolve from Microsoft Solver Foundation
["1103.86"]
pip - Getting Command "python setup.py egg_info" error when ... | command python setup py egg_info failed with error code 1Image Source:
["662.51"]
python - Error installing cx_Oracle on Windows 7 - Stack Overflow | command python setup py egg_info failed with error code 1Image Source:
["1229.96"]
Installing Scrapy - Command "python setup.py egg_info" failed with ... | command python setup py egg_info failed with error code 1Image Source:
["1019.47"]
Qiime Install error : Command "python setup.py egg_info" failed ... | command python setup py egg_info failed with error code 1Image Source:
["1261.97"]
Command "python setup.py egg_info" failed with error code 1 in ... | command python setup py egg_info failed with error code 1Image Source:
["1241.6"]
osx - Command "python setup.py egg_info" failed with error code 1 ... | command python setup py egg_info failed with error code 1Image Source:
["1029.17"]
python - Cannot install pip install pyautogui, error code 1 ... | command python setup py egg_info failed with error code 1Image Source:
["407.4"]
Command "python setup.py egg_info" failed with error code 1 ... | command python setup py egg_info failed with error code 1Image Source: