This is what I have tested with the dis module in python -
>>> def f():
... a = 1
... b = 2
... c = 3
... a = b + c * a
... return a + c
...
>>> dis.dis(f)
2 0 LOAD_CONST 1 (1)
2 STORE_FAST 0 (a)
3 4 LOAD_CONST 2 (2)
6 STORE_FAST 1 (b)
4 8 LOAD_CONST 3 (3)
10 STORE_FAST 2 (c)
5 12 LOAD_FAST 1 (b)
14 LOAD_FAST 2 (c)
16 LOAD_FAST 0 (a)
18 BINARY_MULTIPLY
20 BINARY_ADD
22 STORE_FAST 0 (a)
6 24 LOAD_FAST 0 (a)
26 LOAD_FAST 2 (c)
28 BINARY_ADD
30 RETURN_VALUE
Those are instructions for a stack-based virtual machine. Is there any way to convert the above stack-based instructions into register-based instructions provided I have access to unlimited number of registers.
I only know about one tool which does that, we know that JVM is stack based but Dalvik VM is register-based. When we write code in Java, the class files contain stack based instructions and the dx tool converts the stack based instructions to register based instructions so that it can run in the Dalvik VM. So most probably there could be an algorithm somewhere which I have missed.
Also can there be an edge can where the stack could dynamically grow and shrink(which would be decided in runtime) , in that case it would be impossible to convert stack based instructions to register based. However one tool does it.
Can someone point me to the correct direction. Or know any algorithm which can help in this.
Related
I have a vector
[2 3 4]
That I need to multiply with a matrix
1 1 1
2 2 2
3 3 3
to get
2 3 4
4 6 8
6 9 12
Now, I can make the vector into a matrix and do an element-wise multiplication, but is there also an efficient way to do this in MKL / CBLAS?
Yes, there is a function in oneMKL called cblas_?gemv which computes the multiplication of matrix and vector.
You can refer to the below link for more details regarding the usage of the function.
https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-c/top/blas-and-sparse-blas-routines/blas-routines/blas-level-2-routines/cblas-gemv.html
If you have installed the oneMKL in your system, you can take a look at the examples which helps you to better understand the usage of the functions that are available in the library.
I have a data frame with all columns in discrete format. I apply the following code to generate a BN using bnlearn package. However I get this error that says "score 'bic' may be used with discrete data only" while essentially my data are discrete! Here is a sample of my data:
A B C
3 2 0
0 0 5
5 1 7
0 0 2
4 6 1
And this is what I run:
> test=hc(dat, score="bic")
Error in check.score(score, x) :
score 'bic' may be used with discrete data only.
I don't get why my data is not seen as discrete?
I am new to Spark and Scala. I am working on a Scala project where I will have data access from SQL Server.
There is a table in SQL Server has info about clothes. itemCode is the primary key and several attributes with Boolean value 0/1 - Designer, Exclusive, Handloom and several other columns having attributes of the product etc.
Code Designer Exclusive Handloom
A 1 0 1
B 1 0 0
C 0 0 1
D 0 1 0
E 0 1 0
F 1 0 1
G 0 1 0
H 0 0 0
I 1 1 1
J 1 1 1
K 0 0 1
L 0 1 0
M 0 1 0
N 1 1 0
O 0 1 1
P 1 1 0
and the list continues.
I have to select a collection of 32 items out of 320 items that have ATLEAST:
8 Designer, 8 Exclusive, 8 Handloom, 8 WeddingStyle, 8 PartyStyle,
8 Silk, 8 Georgette
I had solved the problem in MS Excel solver (it uses Gradient Descent algo) by adding an extra column and using sumproduct function between added column and required columns. So, the problem was solved there and it took around 1 minute 30 seconds for the same.
Also, the problem can be solved by writing an SQL query with 32 joins (so many), for example, if i want to select 6 items out of those 16 above with atleast 4 items designer, 4 exclusive, 4 handloom, the query would be like in my post: MYSQL - Select rows fulfilling many count conditions
In production, I have to fetch 32 rows like this way, So my question is how do I proceed further with the project.
I am working on Scala IDE for Eclipse, and have added spark mllib there. I have fetched data via JDBC and stored in a dataframe, and the created a temporary table:
dataFrame.registerTempTable("Data")
There is a class optimizer in mllib optimization that uses gradient descent (like excel solver does) to solve problems. But, that is for machine learning and takes as input training data.
I am not able to understand how do I proceed with my project. Can i use mllib, or use a better simple version of the sql with sparkSQL. I need serious help.
I'd recommend you to use https://spark.apache.org/docs/1.3.0/sql-programming-guide.html#creating-dataframes rather than MLLib.
I solved this problem through linear programming. I have now used lpsolver library for java in my scala project. It is giving almost the same result as in excel solver.
Hi all,
Above you'll see a line-graph plotted with SPSS. I want to improve this line-graph according to its data. Meaning that some elements are not presented correctly:
(1) I deliberately adjusted the scaling on the Y-axis from -1 to 10, in order to notice the breaks (i.e. missing values) in the line graph. Otherwise you'll not notice the breaks, as it will overlap with the bottom-line of the graph. Is it possible to notice the breaks, but with a scaling of 0 to 10 (in SPSS)? > SOLVED
(2) On the X-axis, point 14 and 15 are missing, hence the break. However, the line graph shows an upward trend just after point 13, and a downward trend just before point 16. Is it possible to adjust the line-graph (in SPSS), which would delete these described (interpolation) trends?
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Time_Period_Hours
MEAN(MT)[name="MEAN_MT"] MISSING=VARIABLEWISE REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Time_Period_Hours=col(source(s), name("Time_Period_Hours"), unit.category())
DATA: MEAN_MT=col(source(s), name("MEAN_MT"))
GUIDE: axis(dim(2), delta(1))
SCALE: linear(dim(2), min(-0.5), max(9))
ELEMENT: line(position(Time_Period_Hours*MEAN_MT))
ELEMENT: point(position(Time_Period_Hours*MEAN_MT), color(color.black),
size(size."3px"))
END GPL.
Here is an example, for the line element you need to specify the option missing.gap() - I thought just deleting missing.wings() from the default code would work but maybe it is an internal default. You may want to consider changing Time_Period_Hours to a scale variable and doing the aggregation outside of GGRAPH. Also making the Y axis scale in your example go all the way up to 9 seems a bit superfluous.
DATA LIST FREE / Time_Period_Hours MT.
BEGIN DATA
1 1
2 0
3 0
4 0
5 1
6 0
7 0
8 0
9 0
10 0
11 .
12 0
13 0
14 .
15 .
16 1
17 0
18 0
19 0
20 .
21 0
END DATA.
FORMATS Time_Period_Hours MT (F2.0).
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Time_Period_Hours
MEAN(MT)[name="MEAN_MT"] MISSING=VARIABLEWISE REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Time_Period_Hours=col(source(s), name("Time_Period_Hours"), unit.category())
DATA: MEAN_MT=col(source(s), name("MEAN_MT"))
GUIDE: axis(dim(2), delta(1))
SCALE: linear(dim(2), min(-0.5), max(9))
ELEMENT: line(position(Time_Period_Hours*MEAN_MT), missing.gap())
ELEMENT: point(position(Time_Period_Hours*MEAN_MT), color(color.black),
size(size."3px"))
END GPL.
If you look at the original Wordnet search and select "Display options: Show Lexical File Info", you'll see an extremely useful classification of words called lexical file. Eg for "filling" we have:
<noun.substance>S: (n) filling, fill (any material that fills a space or container)
<noun.process>S: (n) filling (flow into something (as a container))
<noun.food>S: (n) filling (a food mixture used to fill pastry or sandwiches etc.)
<noun.artifact>S: (n) woof, weft, filling, pick (the yarn woven across the warp yarn in weaving)
<noun.artifact>S: (n) filling ((dentistry) a dental appliance consisting of ...)
<noun.act>S: (n) filling (the act of filling something)
The first thing in brackets is the "lexical file". Unfortunately I have not been able to find a SPARQL endpoint that provides this info
The latest RDF translation of Wordnet 3.0 points to two things:
Talis SPARQL endpoint. Use eg this query to check there's no such info:
DESCRIBE <http://purl.org/vocabularies/princeton/wn30/synset-chair-noun-1>
W3C's mapping description. Appendix D "Conversion details" describes something useful: wn:classifiedByTopic.
But it's not the same as lexical file, and is quite incomplete. Eg "chair" has nothing, while one of the senses of "completion" is in the topic "American Football"
DESCRIBE <http://purl.org/vocabularies/princeton/wn30/synset-completion-noun-1> ->
<j.1:classifiedByTopic rdf:resource="http://purl.org/vocabularies/princeton/wn30/synset-American_football-noun-1"/>
The question: is there a public Wordnet query API, or a database, that provides the lexical file information?
Using the Python NLTK interface:
from nltk.corpus import wordnet as wn
for synset in wn.synsets('can'):
print synset.lexname
I don't think you can find it in the RDF/OWL Representation of WordNet. It's in the WordNet distribution though: dict/lexnames. Here is the content of the file as of WordNet 3.0:
00 adj.all 3
01 adj.pert 3
02 adv.all 4
03 noun.Tops 1
04 noun.act 1
05 noun.animal 1
06 noun.artifact 1
07 noun.attribute 1
08 noun.body 1
09 noun.cognition 1
10 noun.communication 1
11 noun.event 1
12 noun.feeling 1
13 noun.food 1
14 noun.group 1
15 noun.location 1
16 noun.motive 1
17 noun.object 1
18 noun.person 1
19 noun.phenomenon 1
20 noun.plant 1
21 noun.possession 1
22 noun.process 1
23 noun.quantity 1
24 noun.relation 1
25 noun.shape 1
26 noun.state 1
27 noun.substance 1
28 noun.time 1
29 verb.body 2
30 verb.change 2
31 verb.cognition 2
32 verb.communication 2
33 verb.competition 2
34 verb.consumption 2
35 verb.contact 2
36 verb.creation 2
37 verb.emotion 2
38 verb.motion 2
39 verb.perception 2
40 verb.possession 2
41 verb.social 2
42 verb.stative 2
43 verb.weather 2
44 adj.ppl 3
For each entry of dict/data.*, the second number is the lexical file info. For example, this filling entry contains the number 13, which is noun.food.
07883031 13 n 01 filling 0 002 # 07882497 n 0000 ~ 07883156 n 0000 | a food mixture used to fill pastry or sandwiches etc.
It can be done through MIT JWI (MIT Java Wordnet Interface) a Java API to query Wordnet. There's a topic in this link showing how to implement a java class to access lexicographic
This is what worked for me,
Synset[] synsets = database.getSynsets(wordStr);
ReferenceSynset referenceSynset = (ReferenceSynset) synsets[i];
int lexicalCode =referenceSynset.getLexicalFileNumber();
Then use above table to deduce "lexnames" e.g. noun.time
If you're on Windows, chances are it is in your appdata, in the local directory. To get there, you will want to open your file browser, go to the top, and type in %appdata%
Next click on roaming, and then find the nltk_data directory. In there, you will have your corpora file. The full path is something like:
C:\Users\yourname\AppData\Roaming\nltk_data\corpora
and lexnames will present under
C:\Users\yourname\AppData\Roaming\nltk_data\corpora\wordnet.