what I am trying to accomplish is using a database of disease facts
symptom(shingles,headache).
symptom(shingles,fever).
symptom(shingles,malaise).
symptom(shingles,headache).
symptom(smallpox,fever).
symptom(smallpox,rash).
and compare it with a list of symptoms from the user. I can currently get the symptoms from the user and add the disease to a list, however, I cant figure out how to loop through the entier database to add all the possible diseases it could be.
start:-
consult(diseases1),
getSymptoms(Symptoms),
write(Symptoms).
welcome:-
write('Welcome to the Disease Diagnostic Center'),nl,nl.
getSymptoms(Symptoms) :-
write('Please enter symptoms now, enter "Done" when finished: ' ),
read_string(user, "\n", "\r", _, Response),
(
Response == "Done"
->
Symptoms = []
;
atom_string(Symptom,Response),
valid_symptom(Symptom,Symptoms)
).
valid_symptom(Symptom,Symptoms) :-
(
symptom(_,Symptom)
->
getSymptoms(Symptoms0),
foreach(symptom(Y,Symptom),write(Y))
;
format('Invalid symptom: `~w''~n',[Symptom]),
getSymptoms(Symptoms0),
Symptoms = Symptoms0
).
So for example, the user enters fever as one of the symptoms, then the list should have in it shingles and smallpox. Currently I am able to write each possible disease to the screen, but I am not sure what to replace write with to be able to add each to a list.
If you have a list with all you diseases, you can filter it by requiring a specific symptom.
All diseases:
all_diseases(Diseases) :-
setof(Disease, Symptom^symptom(Disease, Symptom), Diseases).
?- all_diseases(D).
D = [shingles, smallpox].
Then filter it:
require_symptom(Symptom, Diseases0, Diseases) :-
setof(Disease,
( member(Disease, Diseases0),
symptom(Disease, Symptom)),
Diseases)
*-> true
; Diseases = [].
?- all_diseases(Ds), require_symptom(headache, Ds, D1).
Ds = [shingles, smallpox],
D1 = [shingles].
?- all_diseases(Ds), require_symptom(fever, Ds, D1).
Ds = D1, D1 = [shingles, smallpox].
?- all_diseases(Ds), require_symptom(vomiting, Ds, D1).
Ds = [shingles, smallpox],
D1 = [].
Output the list before and after filtering it.
Related
I am new to r and I am trying to get my mind around the apply function. So far I managed to run my anovas for all the the variables on my data and I got the pairwise comparison.
varlist <- names(dt)[5:length(dt)]
# loop
models <- lapply(X = varlist,
FUN = function(t) lm(formula = paste0("`", t, "` ~ block+irrigation*genotype"), data = dt))
#Name the list of models to the column name
names(models) = varlist
## apply anova to each model stored in the list, models
lapply(models, anova)
#marginal-means-all-variable}
res.model1 <- lapply(models, function(x) pairs(emmeans(x, ~genotype:irrigation)))
res.model1
So far so good, now I want to create a compact letter list so I can use to plot it. Previously I used the following but I can't work out how to apply an lapply function to the following code
CLD = cld(res.model1,
alpha=0.05,
Letters=letters,
adjust="tukey")
I use the CLD data to create graphs
I manage to get the letters with the following code but then I am not getting the full anova table.
tx <- with(dt, interaction(irrigation, genotype)) # determining the factors
model2 <- lapply(varlist, function(x) {
lm(substitute(i~block+tx, list(i = as.name(x))), data = dt)}) # using the factors already in "tx"
lapply(model2, anova)
letters = lapply(model2, function(m) HSD.test((m), "tx", alpha = 0.05, group = TRUE, console = TRUE))
Any suggestions to achieve what I need.
Thank you
I'm creating a shiny app and i'm letting the user choose what data that should be displayed in a plot and a table. This choice is done through 3 different input variables that contain 14, 4 and two choices respectivly.
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(
selectInput(inputId = "DataSource", label = "Data source", choices =
c("Restoration plots", "all semi natural grasslands")),
selectInput(inputId = "Variabel", label = "Variable", choices =
choicesVariables)),
#choicesVariables definition is omitted here, because it's very long but it
#contains 14 string values
selectInput(inputId = "Factor", label = "Factor", choices = c("Company
type", "Region and type of application", "Approved or not approved
applications", "Age group" ))
),
dashboardBody(
plotOutput("thePlot"),
tableOutput("theTable")
))
This adds up to 73 choices (yes, i know the math doesn't add up there, but some choices are invalid). I would like to do this using a lookup table so a created one with every valid combination of choices like this:
rad1<-c(rep("Company type",20), rep("Region and type of application",20),
rep("Approved or not approved applications", 13), rep("Age group", 20))
rad2<-choicesVariable[c(1:14,1,4,5,9,10,11, 1:14,1,4,5,9,10,11, 1:7,9:14,
1:14,1,4,5,9,10,11)]
rad3<-c(rep("Restoration plots",14),rep("all semi natural grasslands",6),
rep("Restoration plots",14), rep("all semi natural grasslands",6),
rep("Restoration plots",27), rep("all semi natural grasslands",6))
rad4<-1:73
letaLista<-data.frame(rad1,rad2,rad3, rad4)
colnames(letaLista) <- c("Factor", "Variabel", "rest_alla", "id")
Now its easy to use subset to only get the choice that the user made. But how do i use this information to plot the plot and table without using a 73 line long ifelse statment?
I tried to create some sort of multidimensional array that could hold all the tables (and one for the plots) but i couldn't make it work. My experience with these kind of arrays is limited and this might be a simple issue, but any hints would be helpful!
My dataset that is the foundation for the plots and table consists of dataframe with 23 variables, factors and numerical. The plots and tabels are then created using the following code for all 73 combinations
s_A1 <- summarySE(Samlad_info, measurevar="Dist_brukcentrum",
groupvars="Companytype")
s_A1 <- s_A1[2:6,]
p_A1=ggplot(s_A1, aes(x=Companytype,
y=Dist_brukcentrum))+geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(aes(ymin=Dist_brukcentrum-se,
ymax=Dist_brukcentrum+se),width=.2,position=position_dodge(.9))+
scale_y_continuous(name = "") + scale_x_discrete(name = "")
where summarySE is the following function, burrowed from cookbook for R
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=TRUE,
conf.interval=.95, .drop=TRUE) {
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
The code in it's entirety is a bit to large but i hope this may clarify what i'm trying to do.
Well, thanks to florian's comment i think i might have found a solution my self. I'll present it here but leave the question open as there is probably far neater ways of doing it.
I rigged up the plots (that was created as lists by ggplot) into a list
plotList <- list(p_A1, p_A2, p_A3...)
tableList <- list(s_A1, s_A2, s_A3...)
I then used subset on my lookup table to get the matching id of the list to select the right plot and table.
output$thePlot <-renderPlot({
plotValue<-subset(letaLista, letaLista$Factor==input$Factor &
letaLista$Variabel== input$Variabel & letaLista$rest_alla==input$DataSource)
plotList[as.integer(plotValue[1,4])]
})
output$theTable <-renderTable({
plotValue<-subset(letaLista, letaLista$Factor==input$Factor &
letaLista$Variabel== input$Variabel & letaLista$rest_alla==input$DataSource)
skriva <- tableList[as.integer(plotValue[4])]
print(skriva)
})
I have written functions to access a database.
The table called Books has:
- a `book_id` TEXT column,
- a title TEXT column, and
- an author TEXT column.
For the first one, run_query is a function that connects the database.
get_book_cnt_per_author is a function that returns a list of tuples in this form:
'author', number of books
I don't know how to use the run_query function through the loop. I always got None for what I wrote.
I don't know where my problem is. I only get one book for each author.
Please tell me what is the problem.
def get_books(db, book_cnt_list, book_cnt):
""" (str, list of tuple, int) -> list of str
Precondition: the elements in book_cnt_list are sorted
in ascending order by author name.
Return a list of all the book titles whose authors
each have book_cnt books in the database with name db
according to the book_cnt_list. The book titles should be in
ascending order for each author, but not for the entire list.
Follow ascending order across authors, that is, the order
authors appear in book_cnt_list that is already sorted by
author name.
>>> author_cnt_list = get_book_cnt_per_author("e7_database.db")
>>> books_list = get_books("e7_database.db", author_cnt_list, 10)
>>> books_list[0]
'A Christmas Carol'
>>> books_list[9]
'The Life and Adventures of Nicholas Nickleby'
>>> books_list[10]
'Disgrace'
>>> books_list[-1]
'Youth'
"""
# HINT: First figure out which authors have book_cnt books
# using the book_cnt_list. Then, access the database db
# to retrieve the required information for those authors.
# Do not call any other of your E7 functions other than run_query.
list1 = []
for i in book_cnt_list:
if i[1] == "book_cnt":
list1.append(i[0])
for j in list1:
return run_query(my_db, '''SELECT title FROM Books OREDER BY Books.title ASC WHERE Books.author = ? ''', (j))
def create_author_dict(db):
""" (str) -> dict of {str: list of str}
Return a dictionary that maps each author to the books they have written
according to the information in the Books table of the database
with name db.
>>> author_dict = create_author_dict('e7_database.db')
>>> author_dict['Isaac Asimov'].sort()
>>> author_dict['Isaac Asimov']
['Foundation', 'I Robot']
>>> author_dict['Maya Angelou']
['I Know Why the Caged Bird Sings']
"""
con = sqlite3.connect(db)
cur = con.cursor()
cur.execute('''SELECT author, title FROM Books WHERE Books.author = ?''')
new_list = cur.fetchall()
new_dict = {}
for i in new_list:
key = i[0]
value = i[1:]
new_dict.update({key: list(value)})
con.commit()
cur.close()
con.close()
return new_dict
try changing the:
new_dict.update({key: list(value)})
for a statement like:
new_dict[key] = new_dict.get(key, list()) + [value]
I'm trying to follow a document that has some code on text mining clustering analysis.
I'm fairly new to R and the concept of text mining/clustering so please bear with me if i sound illiterate.
I create a simple matrix called dtm and then run kmeans to produce 3 clusters. The code im having issues is where a function has been defined to get "five most common words of the documents in the cluster"
dtm0.75 = as.matrix(dt0.75)
dim(dtm0.75)
kmeans.result = kmeans(dtm0.75, 3)
perClusterCounts = function(df, clusters, n)
{
v = sort(colSums(df[clusters == n, ]),
decreasing = TRUE)
d = data.frame(word = names(v), freq = v)
d[1:5, ]
}
perClusterCounts(dtm0.75, kmeans.result$cluster, 1)
Upon running this code i get the following error:
Error in colSums(df[clusters == n, ]) :
'x' must be an array of at least two dimensions
Could someone help me fix this please?
Thank you.
I can't reproduce your error, it works fine for me. Update your question with a reproducible example and you might get a more useful answer. Perhaps your input data object is empty, what do you get with dim(dtm0.75)?
Here it is working fine on the data that comes with the tm package:
library(tm)
data(crude)
dt0.75 <- DocumentTermMatrix(crude)
dtm0.75 = as.matrix(dt0.75)
dim(dtm0.75)
kmeans.result = kmeans(dtm0.75, 3)
perClusterCounts = function(df, clusters, n)
{
v = sort(colSums(df[clusters == n, ]),
decreasing = TRUE)
d = data.frame(word = names(v), freq = v)
d[1:5, ]
}
perClusterCounts(dtm0.75, kmeans.result$cluster, 1)
word freq
the the 69
and and 25
for for 12
government government 11
oil oil 10
I have a little problem with my code. I have a table containing car details, name, price and quantity, so I am trying to create a function called buy which will be used to buy a specific car. When a user buys eg 5 BMW cars, they will call buy_car(bmw,5). Now after this I want to update the new value of quantity for BMW cars.
My attempt is below but I can't seem to work around it, I am new to Erlang.
buy_car(X,Ncars) ->
F = fun() ->
%% ----first i find the number of car X available in the shop
[Xcars] = mnesia:read({car,X}),
Nc = Xcars#car.quantity,
Leftcars = Xcars#car{quantity = Nc - Ncars},
%% ---now we update the database
mnesia:write(Leftcars),
end,
mnesia:transaction(F).
Please help me with how I can write a function that buys cars from the shop.
But your implementation works fine except you added illegal comma after mnesia:write(Leftcars).
Here is code that works (I tried your implementation as buy_car2).
-module(q).
-export([setup/0, buy_car/2, buy_car2/2]).
-record(car, {brand, quantity}).
setup() ->
mnesia:start(),
mnesia:create_table(car, [{attributes, record_info(fields, car)}]),
mnesia:transaction(fun() -> mnesia:write(#car{brand=bmw, quantity=1000}) end).
buy_car(Brand, Ncars) ->
F = fun() ->
[Car] = mnesia:read(car, Brand), % crash if the car is missing
mnesia:write(Car#car{quantity = Car#car.quantity - Ncars})
end,
mnesia:transaction(F).
buy_car2(X,Ncars) ->
F = fun() ->
%% ----first i find the number of car X available in the shop
[Xcars] = mnesia:read({car,X}),
Nc = Xcars#car.quantity,
Leftcars = Xcars#car{quantity = Nc - Ncars},
%% ---now we update the database
mnesia:write(Leftcars)
end,
mnesia:transaction(F).
I would do something like below:
Considering the record is defined as :
-record(car_record, {car, quantity}).
The following function will update the data:
buy_car(X,NCars) ->
Row = #car_record{car = X, quantity = NCars}.
mnesia:ets(fun()-> mnesia:dirty_write(Row) end),
mnesia:change_table_copy_type(guiding_data, node(), disc_copies).
To use the above method, mnesia table must be created as "ram_copies" and with no replication nodes. Also, if there are lot of updates happening, you might not want to copy the ram_copies to disk for every update due to performance issues, rather you will do it in time triggered manner.