Having a problem where after reopening the app the i1-i5 values are all replaced with the user_cur_inv value. Cannot find why this is happening.
Link to the code:
https://github.com/sleedham1991/FantasyTrader/blob/master/src/main.c
These constants are all keys, not values. Make them unique and the problem is solved.
define PERSIST_USER_GP 1000
define PERSIST_MAX_INV 10
define PERSIST_CUR_INV 0
define PERSIST_I1 0
define PERSIST_I2 0
define PERSIST_I3 0
define PERSIST_I4 0
define PERSIST_I5 0
See https://developer.pebble.com/docs/c/Foundation/Storage/#persist_write_int
Related
I have a large number of variables ( both Binary and Continuous). Therefore I have determined a logic to assign some variables set to 0 so that they do not become part of the optimisation process.
For example I have a binary decision variable y[b][t]:
where b varies from 1 to 100
and t from 1 to 5.
I could determine using some logic that y[20][2] onwards to y[100][2] would be 0. I want to assign the fixed value of 0 to these variables y[20][2] onwards to y[100][2] thereby reducing the number of variables in my optimisation problem. While y is a binary decision variable I have other continuous variable as well which I would like to similarly set to 0 in advance.
Is there a way how this can be achieved ? I haven't used Python with CPEX but hear that this can be probably be achieved by setting a lower and upper bound of the variables. Is there a similar method in OPL.
----Added 13th Aug
May be I was not very clear or I could not understand the solution suggested.
What I wanted is say I have the following decision variable Xbmt ...(I have a few of them)
Originally declared as :
dvar float+ Xbmt[PitBlocks][Plants][TimePeriods];
But for some of the PitBlocks and some time periods I want to define this decision variable as 0. Those time periods for which I want to set the decision variable as 0 are defined in a tuple nullVariables. It has block id same as PitBlocks, and it has time_period same as TimePeriod. Hence I want something like below. But I cannot declare the decision variable twice. I need it 0 only for those ids in the nullVariable set.
dvar float+ Xbmt[NullVariablesSet.block_id][Plants][NullVariablesSet.time_period] in 0..0;
How can this be achieved where some of Xbmt remain as decision variables where as some are removed by setting as 0
see https://github.com/AlexFleischerParis/zooopl/blob/master/zoopreassign.mod
within
Making Decision Optimization Simple
int nbKids=300;
{int} seats={40,30}; // how many seats, {} means this is a set
float costBus[seats]=[500,400];
// Now let s see how preassign some decision variables
// Suppose we know that we have exactly 6 buses 40 seats
{int} preassignedseats={40};
int preassignedvalues[preassignedseats]=[6];
dvar int+ nbBus[s in seats]
in
((s in preassignedseats)?preassignedvalues[s]:0)
..
((s in preassignedseats)?preassignedvalues[s]:maxint);
minimize sum(b in seats) costBus[b]*nbBus[b];
subject to
{
sum(b in seats) b*nbBus[b]>=nbKids;
}
I want my predictions in probabilities between 0 and 1. I already did that in xgboost but I wanna try out Lightgbm too but its outputting solid predictions(that is in integer only). I could do that in XGBoost by setting 'objective' parameter to binary:logistic but in Lightgbm there doesn't seem to be any parameter like that, It only has binary and it is giving output in 0 or 1.
To get the class probability between 0 and 1 in lightgbm, you have to use a default value of a parameter "objective" is a regression.
'objective' = 'binary' ( return class label 0 or 1)
'objective' = 'regression' ( return class probability between 0 and 1)
You can do it by setting objective: “multiclass” with num_class: 2 as parameters. The results might not be the same with direct binary classification model yet I can ensure you that there will be no performance loss.
Bonus: As loss metric, you can use “multi_error” or “multi_logloss” or interestingly a combination of both like:
metric: “multi_error”, “multi_logloss”
You can use predict(raw_score=True)
If you are using the sklearn API - You can use objective "binary", just use predict_proba() instead of predict()
As an illustrative example, suppose this is your dataset:
cat sex age
1 1 13
1 0 14
1 1 .
2 1 23
2 1 45
2 1 15
If you want to create a table of frequencies between cat and sex, you tabulate these two variables and you get the following result:
tab cat sex
| sex
cat | 0 1 | Total
-----------+----------------------+----------
1 | 1 2 | 3
2 | 0 3 | 3
-----------+----------------------+----------
Total | 1 5 | 6
I am writing a Stata program where the three variables are involved, i.e. cat, sex and age. Getting the matrix of frequencies for the first two variables is just an intermediate step that I need for further computation.
cap program drop myexample
program def myexample, rclass byable(recall) sortpreserve
version 14
syntax varlist [aweight iweight fweight] [if] [in] [ , AGgregate ]
args var1 var2 var3
tempname F
marksample touse
set more off
if "`aggregate'" == "" {
local var1: word 1 of `varlist'
local var2: word 2 of `varlist'
local var3: word 3 of `varlist'
qui: tab `var1' `var2' [`weight' `exp'] if `touse', matcell(`F') label matcol(`var2')
mat list `F'
}
end
However, when I run:
myexample cat sex age
I get this result which is not what I expected:
__000001[2,2]
c1 c2
r1 1 1
r2 0 3
That is, given that age contains a missing value, even if it is not directly involved in the tabulation, the program ignores the missing value and does not take into account that observation. I need to get the result of the first tabulation. I have tried using summarize instead, but the same problem arises. When implemented inside the program, missing values are not counted.
You are complaining about behaviour which you built into your own program. The responsibility and the explanation are in your hands.
The effect of
marksample touse
followed by calling up a command with the qualifier
if `touse'
is to ignore missing values. marksample by default marks as "to use" those observations in which all variables specified have non-missing values; the other observations are marked as to be ignored. It also takes account of any if or in qualifiers and any zero weights.
It's also true, as #Noobie explains, that omitting missing values from a tabulation is default for tabulate in any case.
So, to get the result you want you'd need to modify your marksample call to
marksample touse, novarlist
and to call up tabulate with the missing option (if it's compulsory) or to allow users to specify a missing option which you then pass to tabulate.
You also ask about summarize. By design that command ignores missing values. I don't know what you would expect summarize to do about them. It could report a count of missing values. If you want that, several other commands will oblige, such as codebook or missings (Stata Journal). You can always include a report on missings in your program, such as using count to count the missings and display the result.
I understand your program to be very much work in progress, so won't comment on details you don't ask about.
This is caused by marksample. Rule 5 in help mark states
The marker variable is set to 0 in observations for which any of the
numeric variables in varlist contain a numeric missing value.
You should use the novarlist option. According to the help file,
novarlist is for use with marksample. It specifies that missing values
among variables in varlist not cause the marker variable to be set to 0.
if I understand well you want tab to include missing values? If so, you just have to ask for it
tab myvar1 myvar2, mi
from the documentation
missing : treat missing values like other values
Lets take this code for example;
If (Random) <> (0 to Variable) then
Its very simple, I just want it to do something if Random is different than 0 to another number set in that variable, im not sure how to do this tho
if you want to check if the variable is not in range from 0 to Variable, it's better to use the code:
if (Random1 < 0) or (Random1 > Variable) then ...
I'm not sure that the code
if not (Random1 in [0..Variable]) then ...
will work for Variable values out of range [0..255]
Sorry, I should of explained better, they are both integer, and Random cant be used since its an instruction, it has to be Random1.
But the answer is
"if not (Random1 in [0..Variable]) then. [0..Variable]"
Thank you very much #lurker.
Is it possible to use DECODE on a SUM field ?
For example, something like this :
DECODE( SUM("QTA' CONV SUM 1 2 2"),'>0',1,'=0',0)
In your specific case, where you want the result to be 1 if the SUM is greater than zero or 0 if the SUM is zero, you can use the Oracle SIGN function. I'm not sure if there's any special Discoverer syntax, but outside of Discoverer I'd do it something like this:
SIGN(SUM(whatever you're summing))
The "techonthenet" site documents the function here.
An alternative to #EdGibbs, if the values that you're summing are integers:
Greatest(Sum(...),1)
If you had negatives to contend with then maybe:
Least(Greatest(Sum(...),1),0)
The syntax gets a bit gnarly so if you did want to stick with Decode or Case then:
Case Sum(...)
When 0 Then 0
Else 1
End
... or ...
Decode(Sum(...),0,0,Sum(...))
I wouldn't go for a solution that repeats the Sum() though. Keep it DRY