I want to transform the following NFSA (see image below) into DFSA.
First let me explain how I usually do it:
I join states to create a new start state (old start state which is 1 here and the states that I can reach using the ε*) and then then from this start state I follow what states I can reach following a then b then c and so on. At the end all the new states that has my old accept states inside them will be my accept states.
So now in my below NFSA it is confusing as I am not sure if I can use '015' as my start state for the DFSA as 5 is an accept state too.
Thanks,
Youssef
Hope this will be help full for your problem.
Related
I am saving the best solution into the DB, and we display that on the web page. I am looking for some solution where a user can add more visits, but that should not change already published trips.
I have checked the documentation and found ProblemFactChange can be used, but only when the solver is already running.
In my case solver is already terminated and the solution is also published. Now I want to add more visits to the vehicle without modifying the existing visits of the Vehicle. Is this possible with Optaplanner? if yes any example of documentation would be very helpful.
You can use PlanningPin annotation for avoiding unwanted changes.
Optaplanner - Pinned planning entities
If you're not looking for pinning (see Ismail's excellent answer), take a look at the OptaPlanner School Timetabling example, which allows adding lessons between solver runs. The lessons simply get stored in the database and then get loaded when the solver starts.
The difficulty with VRP is the chained model complexity (we're working on an alternative): If you add a visit X between A and B, then make sure that afterwards A.next = X, B.previous = X, X.previous = A, X.next = B and X.vehicle = A.vehicle. Not the mention the arrival times etc.
My suggestion would be to resolve what is left after the changes have been introduced. Let's say you are you visited half of your destinations (A -> B -> C) but not yet (C - > D -> E) when two new possible destinations (D' and E') are introduced. Would not this be the same thing as you are starting in C and trying plan for D, D', E and E'? The solution needs to be updated on the progress though so the remainder + changes can be input to the next solution.
Just my two cent.
I have a large Janusgraph database and I'd to create a subgraph centered around one node type and including incoming and outgoing nodes of specific types.
In Cypher, the query would look like this:
MATCH (a:Journal)N-[:PublishedIn]-(b:Paper{paperTitle:'My Paper Title'})<-[:AuthorOf]-(c:Author)
RETURN a,b,c
This is what I tried in Gremlin:
sg = g.V().outE('PublishedIn').subgraph('j_p_a').has('Paper','paperTitle', 'My Paper Title')
.inE('AuthorOf').subgraph('j_p_a')
.cap('j_p_a').next()
But I get a syntax error. 'AuthorOf' and 'PublishedIn' are not the only edge types ending at 'Paper' nodes.
Can someone show me how to correctly execute this query in Gremlin?
As written in your query, the outE step yields edges and the has step will check properties on those edges, following that the query processor will expect an inV not another inE. Without your data model it is hard to know exactly what you need, however, looking at the Cypher I think this is what you want.
sg = g.V().outE('PublishedIn').
subgraph('j_p_a').
inV().
has('Paper','paperTitle', 'My Paper Title').
inE('AuthorOf').
subgraph('j_p_a')
cap('j_p_a').
next()
Edited to add:
As I do not have your data I used my air-routes graph. I modeled this query on yours and used some select steps to limit the data size processed. This seems to work in my testing. Hopefully you can see the changes I made and try those in your query.
sg = g.V().outE('route').as('a').
inV().
has('code','AUS').as('b').
select('a').
subgraph('sg').
select('b').
inE('contains').
subgraph('sg').
cap('sg').
next()
For the millionth time I had a dataset today that listed full state names. But, I needed it to list state postal code abbreviations. Here is a code snip I wrote that mapped the changes for me using data from a generic website.
1) Anyone know of or think of a better solution?
2a) Anyone know of a better web reference? Using USPS sites (such as the ones below) will not seem to work with pd.read_html()
2b) I also had a hard time isolating the correct table from pd.read_html() and the wiki page at: https://en.wikipedia.org/wiki/List_of_U.S._state_abbreviations
import pandas as pd
# Make Generic Data For Demonstration Purpose
data = {'StName':['Wisconsin','Minnesota','Minnesota',
'Wisconsin','Florida','New York']}
df = pd.DataFrame(data)
# Get State Crosswalk From Generic Website
crosswalk = 'http://app02.clerk.org/menu/ccis/Help/CCIS%20Codes/state_codes.html'
states = pd.read_html(crosswalk)[0]
# Demo Crosswalking State Name to State Abbreviation
df['StAbbr'] = df['StName'].map(dict(zip(states['Description'],
states['Code'])))
# Demo Reverse Crosswalking Back to State Name
df['StNameAgain'] = df['StName'].map(dict(zip(states['Code'],
states['Description'])))
I'm trying to train my Wit.ai bot in order to recognize the first name of someone. I'm not very sure if I well understand how the NLP works so I'll give you an example.
I defined a lot of expressions like "My name is XXXX", "Everybody calls me XXXX"
In the "Understanding" table I added an entity named "contact_name" and I add almost 50 keywords like "Michel, John, Mary...".
I put the trait as "free-text" and "keywords".
I'm not sure if this process is correctly. So, I ask you:
does it matter the context like "My name is..." for the NLP? I mean...will it help the bot to predict that after this expression probably a fist name will come on?
is that right to add like 50 values to an entity or it's completly wrong?
what do you suggest as a training process in order to get the first name of someone?
You have done it right by keeping the entity's search strategy as "free-text" and "Keywords". But Adding keywords examples to the entity doesn't make any sense because a person's name is not a keyword.
So, I would recommend a training strategy which is as follows:
Create various templates of the message like, "My name is XYZ", "I am XYZ", "This is XYZ" etc. (all possible introduction messages you could think of)
Remove all keywords and expressions for the entity you created and add these two keywords:
"a b c d e f g h i j k l m n o p q r s t u v w x y z"
"XYZ" (can give any name but maintain this name same for validating the templates)
In the 'Understanding' tab enter the messages and extract the name into the entity ("contact_name" in your case) and validate them
Similarly, validate all message templates keeping the name as "XYZ"
After you have done this for all templates your bot will be able to recognise any name in a given template of the message
The logic behind this is your entity is a free-text and keyword which means it first tries to match the keyword if not matched it tries to find the word in the same position of the templates. Keeping the name same for validations helps to train the bot with the templates and learn the position where the name will be usually found.
Hope this works. I have tried this and worked for me. I am not sure how bot trains in background. I recommend you to start a new app and do this exercise.
Comment if there is any problem.
wit.ai has a pre-trained entity extraction method called wit/contact, which
Captures free text that's either the name or a clear reference to a
person, like "Paul", "Paul Smith", "my husband", "the dentist".
It works good even without any training data.
To read about the method refer to duckling.
Is it possible to handle relations between jobs (in this case services) in jsprit?
For instance one job has to start at the same time with one or more other jobs.
Or one job must not start before the end of another job (normal sequence).
If not, do you know other java libraries that can handle such kind of restrictions?
Thank you!
Yes you can handle such relations with jsprit.
It is a bit of coding, but - I think - easy to implement. To illustrate it, I prepared you five examples that are based on the Vehicle Routing Problems (VRP) developed by Christofides-Mingozzi-Toth's* (first benchmarking instance). Follow the links behind the individual headlines and you get to the code. Note that the first activity in a route is marked with a triangle. The labels denote jobIds.
noConstraints
capacityConstraints
job 13 and 21 in same route
job 13 and 21 in same route AND 21 before 13
job 13 and 21 in same route AND 13 right after 21
Visit http://jsprit.github.io/ and you get to a number of code examples and docs.
*N. Christofides, A. Mingozzi, and P. Toth. The vehicle routing problem. In N. Christofides, A. Mingozzi, P. Toth, and C. Sandi, editors, Combinatorial Optimization. John Wiley, Chichester, 1979.