I have the following code where firstly I add the values for each index from two columns and creating Vector{Int64}
df = CSV.read(joinpath("data", "data.csv"), DataFrame)
adding_columns = df.firstcolumn + df.secondcolumn
Then I will create a function as following:
function fnct(data::Vector{T}; var= 8) where { T <: Number }
V = []
for x in 1:size(data)[1]
strt = x-var
ending = x+var
avg = 0
if strt < 1
for y in 1:x+var
avg = avg+data[y]
end
avg = avg/(x+var-1)
elseif ending > size(data)[1]
for y in x-var:size(data)[1]
avg = avg+data[y]
end
avg = avg/(size(data)-x-var)
else
for y in x-var:x+var
avg = avg+data[y]
end
avg = avg/(2*var)
end
push!(V,avg)
end
return V
end
When trying:
typeof(adding_columns)
I will get:
Vector{Int64}
however when calling
fnct(adding_columns)
I will get:
ERROR: MethodError: no method matching -(::Tuple{Int64}, ::Int64)
I presume that it takes my adding_columns as Tuple but I do not get it why, when the typeof is giving me Vector.
How could I solve this problem?
size(data) is a tuple:
julia> size([1,2,3]::Vector{Int})
(3,)
...but you're subtracting an integer from it in avg = avg/(size(data)-x-var).
Did you mean avg = avg/(length(data)-x-var) or avg = avg/(size(data, 1)-x-var)?
Related
output = np.empty([17157,4])
for every row in data
for rows in data:
initializing variables
snowfall = 0
positive_temp = 0
mass_balance = 0
melt = 0
for every cell in a row
for columns in range(12):
if rows[columns+2] < 0:
snowfall += rows[columns+14]
else:
positive_temp += rows[columns+2]
melt += positive_temp * 7
mass_balance += snowfall - melt
lat = rows[0]
lon = rows[1]
elev = rows[26]
appending values to output
np.append(output, ([lat, lon, mass_balance, elev]), axis = 0)
I'm trying to use Julia to solve the common tile game 15 Puzzle using Julia using A* algorithm. I am quite new to the language and my style may seem very C like. When I try the following code, I run out of memory. I'm not sure if its related to the use of a pointer style in my structs or just bad design.
struct Node
parent
f::Int64
board::Array{Int64,1}
end
function findblank(A::Array{Int64,1})
x = size(A,1)
for i = 1:x
if A[i] == x
return i
end
end
return -1
end
function up(A::Array{Int64,1})
N = size(A,1)
Nsq = isqrt(N)
blank = findblank(A)
B = copy(A)
if blank / Nsq <= 1
return nothing
end
B[blank-Nsq],B[blank] = B[blank],B[blank-Nsq]
return B
end
function down(A::Array{Int64,1})
N = size(A,1)
Nsq = isqrt(N)
blank = findblank(A)
B = copy(A)
if (blank / Nsq) > (Nsq -1)
return nothing
end
B[blank+Nsq],B[blank] = B[blank],B[blank+Nsq]
return B
end
function left(A::Array{Int64,1})
N = size(A,1)
Nsq = isqrt(N)
blank = findblank(A)
B = copy(A)
if (blank % Nsq) == 1
return nothing
end
B[blank-1],B[blank] = B[blank],B[blank-1]
return B
end
function right(A::Array{Int64,1})
N = size(A,1)
Nsq = isqrt(N)
blank = findblank(A)
B = copy(A)
if (blank % Nsq) == 0
return nothing
end
B[blank+1],B[blank] = B[blank],B[blank+1]
return B
end
function manhattan(A::Array{Int64,1})
N = size(A,1)
Nsq = isqrt(N)
r = 0
for i in 1:N
if (A[i]==i || A[i]==N)
continue
end
row1 = floor((A[i]-1) / Nsq)
col1 = (A[i]-1) % Nsq
row2 = floor((i-1) / Nsq)
col2 = (i-1) % Nsq
r+= abs(row1 - row2) + abs(col1 - col2)
end
return r
end
# start = [1,2,3,4,5,6,7,9,8]
# start = [6,5,4,1,7,3,9,8,2] #26 moves
start = [7,8,4,11,12,14,10,15,16,5,3,13,2,1,9,6] # 50 moves
goal = [x for x in 1:length(start)]
# println("The manhattan distance of $start is $(manhattan(start))")
g = 0
f = g + manhattan(start)
pq = PriorityQueue()
actions = [up,down,left,right]
dd = Dict{Array{Int64,1},Int64}()
snode = Node(C_NULL,f,start)
enqueue!(pq,snode,f)
pos_seen = 0
moves = 0
while (!isempty(pq))
current = dequeue!(pq)
if haskey(dd,current.board)
continue
else
push!(dd, current.board =>current.f)
end
if (current.board == goal)
while(current.board != start)
println(current.board)
global moves +=1
current = current.parent[]
end
println(start)
println("$start solved in $moves moves after looking at $pos_seen positions")
break
end
global pos_seen+=1
global g+=1
for i in 1:4
nextmove = actions[i](current.board)
if (nextmove === nothing || nextmove == current.board || haskey(dd,nextmove))
continue
else
global f = g+manhattan(nextmove)
n = Node(Ref(current),f,nextmove)
enqueue!(pq,n,f)
end
end
end
println("END")
I'm trying to plot a function of two variables with pyplot in Julia. The working starting-point is the following (found here at StackOverflow):
function f(z,t)
return z*t
end
z = linspace(0,5,11)
t = linspace(0,40,4)
for tval in t
plot(z, f(z, tval))
end
show()
This works right for me and is giving me exactly what I wanted:
a field of lines.
My own functions are as follows:
## needed functions ##
const gamma_0 = 6
const Ksch = 1.2
const Kver = 1.5
function Kvc(vc)
if vc <= 0
return 0
elseif vc < 20
return (100/vc)^0.1
elseif vc < 100
return 2.023/(vc^0.153)
elseif vc == 100
return 1
elseif vc > 100
return 1.380/(vc^0.07)
else
return 0
end
end
function Kgamma(gamma_t)
return 1-((gamma_t-gamma_0)/100)
end
function K(gamma_t, vc)
return Kvc(vc)*Kgamma(gamma_t)*Ksch*Kver
end
I've tried to plot them as follows:
i = linspace(0,45,10)
j = linspace(0,200,10)
for i_val in i
plot(i,K(i,j))
end
This gives me the following Error:
isless has no method matching isless(::Int64, ::Array{Float64,1})
while loading In[51], in expression starting on line 3
in Kvc at In[17]:2 in anonymous at no file:4
Obviously, my function cant deal with an array.
Next try:
i = linspace(0,200,11)
j = linspace(0,45,11)
for i_val in i
plot(i_val,map(K,i_val,j))
end
gives me a empty plot only with axes
Can anybody please give me a hint...
EDIT
A simpler example:
using PyPlot
function P(n,M)
return (M*n^3)/9550
end
M = linspace(1,5,5)
n = linspace(0,3000,3001)
for M_val in M
plot(n,P(n,M_val))
end
show()
Solution
OK, with your help I found this solution for the shortened example which works for me as intended:
function P(n,M)
result = Array(Float64, length(n))
for (idx, val) in enumerate(n)
result[idx] = (M*val^3)/9550
end
return result
end
n = linspace(0,3000,3001)
for M_val = 1:5
plot(n,P(n,M_val))
end
show()
This gives me what I wanted for this shortened example. The remainig question is: could it be done in a simpler more elegant way?
I'll try to apply it to the original example and post it when I'll succed.
I don't completely follow all the details of what you're trying to accomplish, but here are examples on how you can modify a couple of your functions so that they accept and return arrays:
function Kvc(vc)
result = Array(Float64, length(vc))
for (idx, val) in enumerate(vc)
if val <= 0
result[idx] = 0
elseif val < 20
result[idx] = (100/val)^0.1
elseif val < 100
result[idx] = 2.023/(val^0.153)
elseif val == 100
result[idx] = 1
elseif val > 100
result[idx] = 1.380/(val^0.07)
else
result[idx] = 0
end
end
return result
end
function Kgamma(gamma_t)
return ones(length(gamma_t))-((gamma_t - gamma_0)/100)
end
Also, for your loop, I think you probably want something like:
for i_val in i
plot(i_val,K(i_val,j))
end
rather than plot(i, K(i,j), as that would just print the same thing over and over.
< is defined for scalars. I think you need to broadcast it for arrays, i.e. use .<. Example:
julia> x = 2
2
julia> x < 3
true
julia> x < [3 4]
ERROR: MethodError: no method matching isless(::Int64, ::Array{Int64,2})
Closest candidates are:
isless(::Real, ::AbstractFloat)
isless(::Real, ::Real)
isless(::Integer, ::Char)
in <(::Int64, ::Array{Int64,2}) at .\operators.jl:54
in eval(::Module, ::Any) at .\boot.jl:234
in macro expansion at .\REPL.jl:92 [inlined]
in (::Base.REPL.##1#2{Base.REPL.REPLBackend})() at .\event.jl:46
julia> x .< [3 4]
1x2 BitArray{2}:
true true
I have a problem here... if I have a table with few repeated string results. I want to know the value am the ammount of each.
For example. A function return an unknown "letters" and with unknown quantities in quantity
Function () return Table end
Table ={'a','a','c','b','b','a',...}
And I want to get this.
table.a={'a','a','a'}
table.b={'b','b'}
table.c={'c'}
....
....
I have no clue how to solve it...
Write a function, which creates a hash map of these things:
function RepetitionCounter(tInput)
local tCounter = {}
for i, v in ipairs(tInput) do
tCounter[v] = (tCounter[v] or 0) + 1
end
return tCounter
end
which you'll use as follows:
local tData = {'a','a','c','b','b','a',...}
local tCounts = RepetitionCounter(tData)
and the table tCounts would be as follows:
tCounts.a = 3
tCounts.b = 2
tCounts.c = 1
Modifying the function above by just a little, you can get the desired output. Replace the following line:
tCounter[v] = (tCounter[v] or 0) + 1
with
if not tCounter[v] then
tCounter[v] = {}
else
table.insert(tCounter[v], v)
end
I have a Julia data frame where one column is called 'close' and I want to add another column to the data frame called 'sma' which is a simple moving average of 'close'. Thanks to anyone who can help!
I noticed a problem in the code amrod. It doesn't account for the first length of SMA that doesn't have enough previous data points for a good SMA and also gives double the SMA that is asked for. I changed it to input zeros up to that point, I also changed the variable names when I was figuring out how it works.
function makeSMA(data, SMA)
len = length(data)
y = Vector{Float64}(len)
for i in 1:SMA-1
y[i] = NaN
end
for i in SMA:len
y[i] = mean(data[i-(SMA-1):i])
end
return y
end
check this:
function ma{T <: Real}(x::Vector{T}, wind::Int)
len = length(x)
y = Vector{Float64}(len)
for i in 1:len
lo = max(1, i - wind)
hi = min(len, i + wind)
y[i] = mean(x[lo:hi])
end
return y
end
x = collect(1:100)
y = ma(x, 4)
then you can hcat(x, y).
EDIT:
If you want a backwards-looking MA you can use something like
function ma{T <: Real}(x::Vector{T}, wind::Int)
len = length(x)
y = Vector{Float64}(len)
for i in 1:len
if i < wind
y[i] = NaN
else
y[i] = mean(x[i - wind + 1:i])
end
end
return y
end