So I have two vector of vectors like:
matchings_vec = Vector{Vector{Vector{Vector{Any}}}}(num_tilt1)
Minfoall_vec = Vector{Vector{Vector{Vector{Vector{Vector{Float64}}}}}}(num_tilt1)
I defined it as:
matchings_vec = [Vector{Any}() for i in 1:num_tilt1]
Minfoall_vec = [Vector{Float64}() for i in 1:num_tilt1]
Then I am trying to resize the inner vectors like:
for tt = 1:num_tilt1
t_tt = t[tt] #; t1 = t_tt; t2 = 1;
if t_tt == 1
num_rot1 = 1
else
num_rot1 = round(Int, num_rot_t2 * t_tt/2)
if rem.(num_rot1, 2) == 1.0
num_rot1 += 1
end
num_rot1 /= 2
end
num_rot1 = round(Int, num_rot1)
#resize!(matchings_vec[tt], num_rot1)
matchings_vec[tt] = Vector{Any}(num_rot1)
#resize!(Minfoall_vec[tt], num_rot1)
Minfoall_vec[tt] = Vector{Float64}(num_rot1)
for rr = 1:num_rot1
num_tilt2 = round(Int, num_tilt2)
#resize!(matchings_vec[tt][rr], num_tilt2)
matchings_vec[tt][rr] = Vector{Any}(num_tilt2)
#resize!(Minfoall_vec[tt][rr], num_tilt2)
Minfoall_vec[tt][rr] = Vector{Float64}(num_tilt2)
for tt2 = 1:num_tilt2
t_im2 = t[tt2]
#t_im2_1 = t_im2; t_im2_2 = 1;
if t_im2 == 1
num_rot1_2 = 1
else
num_rot1_2 = round(Int, num_rot_t2 * t_im2/2)
if rem.(num_rot1_2, 2) == 1.0
num_rot1_2 += 1
end
num_rot1_2 /= 2
end
num_rot1_2 = round(Int, num_rot1_2)
#resize!(matchings_vec[tt][rr][tt2], num_rot1_2)
matchings_vec[tt][rr][tt2] = Vector{Any}(num_rot1_2)
#resize!(Minfoall_vec[tt][rr][tt2], num_rot1_2)
Minfoall_vec[tt][rr][tt2] = Vector{Float64}(num_rot1_2)
end
end
end
I am getting this error:
MethodError: Cannot convert an object of type Array{Float64,1} to an
object of type Float64 This may have arisen from a call to the
constructor Float64(...), since type constructors fall back to convert
methods.
Stacktrace: [1] setindex!(::Array{Float64,1}, ::Array{Float64,1},
::Int64) at ./array.jl:578 [2] macro expansion at ./In[32]:22
[inlined] [3] anonymous at ./:?
Can you suggest what I am doing wrong? Thanks!
Related
i am trying to use a code in a paper but the code for gurobi seems changed these days, it showed some error and i want to put the gurobi environment setting below into my optimization.
the gurobi environment setting is below :
function setup_gurobi_env(; quiet_mode = true, method_type = :barrier, use_time_limit = true, time_limit = 60.0)
env = Gurobi.Env()
if quiet_mode
setparams!(env; OutputFlag = 0)
end
if method_type == :barrier
setparams!(env; Method = 2)
elseif method_type == :method3
setparams!(env; Method = 3)
elseif method_type != :default
error("Enter a valid method type for Gurobi.")
end
if use_time_limit
setparams!(env; TimeLimit = time_limit)
end
return env
end
the author of the paper use the method below to use this setting:
function portfolio_simplex_jump_setup(Sigma::Matrix{Float64}, gamma::Float64; gurobiEnv = setup_gurobi_env(method_type = :default, use_time_limit = false))
(d, d2) = size(Sigma)
if d != d2
error("Sigma dimensions don't match")
end
mod = Model(with_optimizer(Gurobi.Optimizer, gurobiEnv))
#variable(mod, w[1:d] >= 0)
#constraint(mod, sum(w[i] for i = 1:d) <= 1)
#constraint(mod, w'*Sigma*w <= gamma)
function local_portfolio_oracle(c::Vector{Float64})
#objective(mod, Min, dot(c, w))
optimize!(mod)
z_ast = objective_value(mod)
w_ast = value.(w)
return (z_ast, w_ast)
end
return c -> local_portfolio_oracle(c)
end
i changed the function into this but it still showed error for not be able to use gurobi since my coding is too old.
function portfolio_simplex_jump_setup(; gurobiEnv = setup_gurobi_env(method_type = :default, use_time_limit = false))
mod = Model(Gurobi.Optimizer)
#variable(mod, 0 <=w[1:d] <= 1)
#constraint(mod, sum(w[i] for i = 1:d) <= 3)
function local_portfolio_oracle(c::Vector{Float64})
#objective(mod, Min, dot(c, w))
optimize!(mod)
z_ast = objective_value(mod)
w_ast = value.(w)
return (z_ast, w_ast)
end
return c -> local_portfolio_oracle(c)
end
i think the problem is in here
mod = Model(with_optimizer(Gurobi.Optimizer, gurobiEnv))
maybe gurobi just upload the new coding method?
Thank you to everyone who replied to me~
This is the current pattern to use Gurobi (taken from one of my academic codes):
const GRB_ENV = Gurobi.Env()
m = Model(()->Gurobi.Optimizer(GRB_ENV))
set_optimizer_attribute(m, "OutputFlag", 0)
set_optimizer_attribute(m, "TimeLimit", 100)
set_optimizer_attribute(m, "MIPGap", 0.001)
set_optimizer_attribute(m, "Threads", min(length(Sys.cpu_info()),16))
I am writing a code in SAS and now I need to convert it into R
i have pasted my r code , but i know this is not correct. I need to use something like "Select in SAS" . Please help me to
convert this sas code in R
data t2; set t; by nhi ass_yr ass_mth;
R_G_Right = G1_Right;
M_G_Right = G1_Right_Maculopathy;
R_G_Left = G1_Left;
M_G_Left = G1_Left_Maculopathy;
* if G2 performed, use this ;
if G2_Right ne . then R_G_Right = G2_Right;
if G2_Right_Maculopathy ne . then M_G_Right = G2_Right_Maculopathy;
if G2_Left ne . then R_G_Left = G2_Left;
if G2_Left_Maculopathy ne . then M_G_Left = G2_Left_Maculopathy;
* collapse grades ;
* 0 - remains 0 ;
* 1-2 -> 1 ;
* 3 -> 2 ;
* 4-5-6 -> 3 ;
select (R_G_Right);
when (.) retgradeR = 0;
when (0) retgradeR = 0;``
when (1) retgradeR = 1;
when (2) retgradeR = 1;`enter code here`
when (3) retgradeR = 2;
otherwise retgradeR = 3;
end;
You can do it this way using dplyr
I did not translate the by statement in R because it does not have any impact later in the code.
Basically everything can be turned into a mutate function, and case_when replaces the select SAS code.
library(dplyr)
t2 <- t %>% mutate(R_G_Right = G1_Right,
M_G_Right = G1_Right_Maculopathy,
R_G_Left = G1_Left,
M_G_Left = G1_Left_Maculopathy,
R_G_Right = ifelse(!is.na(G2_Right),G2_Right,R_G_Right),
M_G_Right = ifelse(!is.na(G2_Right_Maculopathy),G2_Right_Maculopathy,M_G_Right),
R_G_Left = ifelse(!is.na(G2_Left),G2_Left,R_G_Left),
M_G_Left = ifelse(!is.na(G2_Left_Maculopathy),G2_Left_Maculopathy,M_G_Left),
retgradeR = case_when(
is.na(R_G_Right)| R_G_Right==0 ~ 0,
R_G_Right %in% c(1,2) ~ 1,
R_G_Right==3 ~2,
TRUE ~ 3)
)
How do you guys handle console input validation? In C++, case/switch is my goto...
I was trying a recursive function but was getting locked in lower levels. Plus that might be overdoing it. I did manage a while loop with an "exclusive or" but, that is not really scalable.
function prob6()
println("Pick a number; any number:")
x = readline(stdin)
y = parse(Int64, x)
z = 0
println("Select 1 or 2")
p1 = readline(stdin)
p2 = parse(Int64, p1)
select = p2
while xor((p2 == 1), (p2 == 2)) == false
println("Select 1 or 2")
p1 = readline(stdin)
p2 = parse(Int64, p1)
select = p2
end
if select == 1
for i in 1:y
print("$i ")
z = z + i
end
else
z = 1
for i in 1:y
print("$i ")
z = z * i
end
end
println(z)
end
Any alternatives?
There are many ways. I usually create a validation loop to check the type of the input item, and will use tryparse instead of parse, since it will not throw an error if input is malformed:
function queryprompt(query, typ)
while true
print(query, ": ")
choice = uppercase(strip(readline(stdin)))
if (ret = tryparse(typ, choice)) != nothing
return ret
end
println()
end
end
n = queryprompt("Integer please", Int64)
println(n)
x = queryprompt("Float please", Float64)
println(x)
Im using chainer and im try to do topic modeling. The code for the training phase contains the following:
optimizer = O.Adam()
optimizer.setup(self.train_model)
clip = chainer.optimizer.GradientClipping(5.0)
optimizer.add_hook(clip)
j = 0
msgs = defaultdict(list)
for epoch in range(epochs):
print "epoch : ",epoch
data = prepare_topics(cuda.to_cpu(self.train_model.mixture.weights.W.data).copy(),
cuda.to_cpu(self.train_model.mixture.factors.W.data).copy(),
cuda.to_cpu(self.train_model.sampler.W.data).copy(),
self.words)
top_words = print_top_words_per_topic(data)
if j % 100 == 0 and j > 100:
coherence = topic_coherence(top_words)
for j in range(self.n_topics):
print j, coherence[(j, 'cv')]
kw = dict(top_words=top_words, coherence=coherence, epoch=epoch)
data['doc_lengths'] = self.doc_lengths
data['term_frequency'] = self.term_frequency
for d, f in utils.chunks(self.batchsize, self.doc_ids, self.flattened):
t0 = time.time()
self.train_model.cleargrads()
l = self.train_model.fit_partial(d.copy(), f.copy(), update_words = update_words, update_topics = update_topics)
prior = self.train_model.prior()
loss = prior * self.fraction
loss.backward()
optimizer.update()
msg = ("J:{j:05d} E:{epoch:05d} L:{loss:1.3e} "
"P:{prior:1.3e} R:{rate:1.3e}")
prior.to_cpu()
loss.to_cpu()
t1 = time.time()
dt = t1 - t0
rate = self.batchsize / dt
msgs["E"].append(epoch)
msgs["L"].append(float(l))
j += 1
logs = dict(loss=float(l), epoch=epoch, j=j, prior=float(prior.data), rate=rate)
print msg.format(**logs)
print "\n ================================= \n"
#serializers.save_hdf5("lda2vec.hdf5", self.model)
msgs["loss_per_epoch"].append(float(l))
whn i execute the code i get for example:
J:00200 E:00380 L:0.000e+00 P:-2.997e+04 R:2.421e+04
only the L(loss) dont change, can someone please help to know why this value remain zero?
in below script i am getting the error
if not any(d['KEY'] == key_str for d in records):
NameError: global name 'key_str' is not defined
I am transposing a input file set of columns to rows and rows to columns.
please help:
if "Sales-Target" in fdmContext["LOCNAME"]:
filename = fdmContext["FILENAME"]
dim_cols = 7
period_dim_index = 0
input_filename = (os.path.abspath("E:/FDMEE/")+"\%s" % filename)
months = ['M%s' % x for x in range(1,13)]
first_row = True
headers = []
records = []
output_filename = "%s_out.%s" % (input_filename.split(".")[0],input_filename.split(".")[1])
input_f = open(input_filename)
for line in input_f:
if first_row:
headers = line.split(",")
headers = [x.rstrip() for x in headers]
else:
data = line.split(",")
for i in range(dim_cols,len(headers)):
key = data[:dim_cols]
del key[period_dim_index]
key.append(headers[i])
key_str = "_".join(key)
if not any(d['KEY'] == key_str for d in records):
record = {}
record["ACCOUNT"] = headers[i]
record["KEY"] = key_str
record["PERIODS"] = {}
for j in range(0,dim_cols):
if j == period_dim_index:
record["PERIODS"][data[j]] = data[i].rstrip()
else:
record[headers[j]] = data[j]
records.append(record)
else:
record = (d for d in records if d["KEY"] == key_str).next()
record["PERIODS"][data[period_dim_index]] = data[i].rstrip()
first_row = False
input_f.close()
output_f = open(output_filename,"w")
row=[]
[row.append(x) for x in headers[:dim_cols] if headers.index(x) != period_dim_index]
row.append("ACCOUNT")
[row.append(x) for x in months]
output_f.write("%s\n" % ",".join(row))
for record in records:
row = [record[x] for x in headers[:dim_cols] if headers.index(x) != period_dim_index]
row.append(record["ACCOUNT"])
for month in months:
if month in record["PERIODS"].keys():
row.append(record["PERIODS"][month])
else:
row.append("0")
output_f.write("%s\n" % ",".join(row))
output_f.close()