I'm starting with Python and I have a basic question with "for" loop
I have two array which contains a values of a same variables:
A = data_lac[:,0]
In the first array, I have values of area and in the second on, values of mean depth.
I would like to find a way to automatize my calculation with different value of a parameter. The equation is the following one:
g= (np.sqrt(A/pi))/n
Here I can calculte my "g" for each row. Now I want to have a loop with differents values of "n". I did this:
i=0
while i <= len(A)-1:
for n in range(2,6):
g[i] = (np.sqrt(A[i]/pi))/n
i += 1
break
In this case, I just have one column with the calculation for n = 2 but not the following one. I tried to add a second dimension to my array but I have an error message saying that I have too many indices for array.
In other, I would like this array:
g[len(A),5]
g has 5 columns each one calculating with a different "n"
Any tips would be very helpful,
Thanks
Update of the code:
data_lac=np.zeros((106,7))
data_lac[:,0:2]=np.loadtxt("/home...", delimiter=';', skiprows=1, usecols=(0,1))
data_lac[:,1]=data_lac[:,1]*0.001
#Initialisation
A = data_lac[:,0]
#example for A with 4 elements
A=[2.1, 32.0, 4.6, 25]
g = np.zeros((len(A),))
I believe you share the indexes within both loops. You were increasing the i (index for the upper while loop) inside the inner for loop (which index with n).
I guess you have A (1 dim array) and you want to produce G (2 dim array) with size of (Len(A, 5))
I am not sure I'm fully understand your require output but I believe you want something like:
i=0
while i <= len(A)-1:
for n in range(2,6):
g[i][n-2] = (np.sqrt(A[i]/pi))/n # n-2 is to get first index as 0 and last as 4
i += 1 # notice the increace of the i is for the upper while loop
break
Important - remember that in python indentation means a lot -> so make sure the i +=1 is under the while scope and not indent to be inside the for loop
Notice - G definition should be as:
g = np.zeros((len(A),4), dtype=float)
The way you define it (without the 4) cause it to be 1 dim array and not 2-dim
Related
I'd like to implement a cellular automaton (CA) in Julia. Dimensions should be wrapped, this means: the left neighbor of the leftmost cell is the rightmost cell etc.
One crucial question is: how to get the neighbors of one cell to compute it's state in the next generation? As dimensions should be wrapped and Julia does not allow negative indices (as in Python) i had this idea:
Considered a 1D CA, one generation is a one-dimensional array:
0 0 1 0 0
What if we create a two dimensional Array, where the first row is shifted right and the third is shifted left, like this:
0 0 0 1 0
0 0 1 0 0
0 1 0 0 0
Now, the first column contain the states of the first cell and it's neighbors etc.
i think this can easily be generalized for two and more dimensions.
First question: do you think this is a good idea, or is this a wrong track?
EDIT: Answer to first question was no, second Question and code example discarded.
Second question: If the approach is basically ok, please have a look at the following sketch:
EDIT: Other approach, here is a stripped down version of a 1D CA, using mod1() for getting neighborhood-indices, as Bogumił Kamiński suggested.
for any cell:
- A array of all indices
- B array of all neighborhood states
- C states converted to one integer
- D lookup next state
function digits2int(digits, base=10)
int = 0
for digit in digits
int = int * base + digit
end
return int
end
gen = [0,0,0,0,0,1,0,0,0,0,0]
rule = [0,1,1,1,1,0,0,0]
function nextgen(gen, rule)
values = [mod1.(x .+ [-1,0,1], size(gen)) for x in 1:length(gen)] # A
values = [gen[value] for value in values] # B
values = [digits2int(value, 2) for value in values] # C
values = [rule[value+1] for value in values] # D
return values
end
for _ in 1:100
global gen
println(gen)
gen = nextgen(gen, rule)
end
Next step should be to extend it to two dimensions, will try it now...
The way I typically do it is to use mod1 function for wrapped indexing.
In this approach, no matter what dimensionality of your array a is then when you want to move from position x by delta dx it is enough to write mod1(x+dx, size(a, 1)) if x is the first dimension of an array.
Here is a simple example of a random walk on a 2D torus counting the number of times a given cell was visited (here I additionally use broadcasting to handle all dimensions in one expression):
function randomwalk()
a = zeros(Int, 8, 8)
pos = (1,1)
for _ in 1:10^6
# Von Neumann neighborhood
dpos = rand(((1,0), (-1,0), (0,1), (0,-1)))
pos = mod1.(pos .+ dpos, size(a))
a[pos...] += 1
end
a
end
Usually, if the CA has cells that are only dependent on the cells next to them, it's simpler just to "wrap" the vector by adding the last element to the front and the first element to the back, doing the simulation, and then "unwrap" by taking the first and last elements away again to get the result length the same as the starting array length. For the 1-D case:
const lines = 10
const start = ".........#........."
const rules = [90, 30, 14]
rule2poss(rule) = [rule & (1 << (i - 1)) != 0 for i in 1:8]
cells2bools(cells) = [cells[i] == '#' for i in 1:length(cells)]
bools2cells(bset) = prod([bset[i] ? "#" : "." for i in 1:length(bset)])
function transform(bset, ruleposs)
newbset = map(x->ruleposs[x],
[bset[i + 1] * 4 + bset[i] * 2 + bset[i - 1] + 1
for i in 2:length(bset)-1])
vcat(newbset[end], newbset, newbset[1])
end
const startset = cells2bools(start)
for rul in rules
println("\nUsing Rule $rul:")
bset = vcat(startset[end], startset, startset[1]) # wrap ends
rp = rule2poss(rul)
for _ in 1:lines
println(bools2cells(bset[2:end-1])) # unwrap ends
bset = transform(bset, rp)
end
end
As long as only the adjacent cells are used in the simulation for any given cell, this is correct.
If you extend this to a 2D matrix, you would also "wrap" the first and last rows as well as the first and last columns, and so forth.
To use vcat(a,b) and hcat(a,b), one must match the number of columns or number of rows in the matrices a and b.
When constructing a matrix using vact(a, b) or hcat(a, b) in a loop, one needs an initial matrix a (like a starting statement). Although all the sub-matrices are created in the same manner, I might need to construct this initial matrix a outside of the loop.
For example, if the loop condition is for i in 1:w, then I would need to pre-create a using i = 1, then start the loop with for i in 2:w.
If there is a nested loop, then my method is very awkward. I have thought the following methods, but it seems they don't really work:
Use a dummy a, delete a after the loop. From this question, we cannot delete row in a matrix. If we use another variable to refer to the useful rows and columns, we might waste some memory allocation.
Use reshape() to make an empty dummy a. It works for 1 dimension, but not multiple dimensions.
julia> a = reshape([], 2, 0)
2×0 Array{Any,2}
julia> b = hcat(a, [3, 3])
2×1 Array{Any,2}:
3
3
julia> a = reshape([], 2, 2)
ERROR: DimensionMismatch("new dimensions (2,2) must be consistent with array size 0")
in reshape(::Array{Any,1}, ::Tuple{Int64,Int64}) at ./array.jl:113
in reshape(::Array{Any,1}, ::Int64, ::Int64, ::Vararg{Int64,N}) at ./reshapedarray.jl:39
So my question is how to work around with vcat() and hcat() in a loop?
Edit:
Here is the problem I got stuck in:
There are many gray pixel images. Each one is represented as a 20 by 20 Float64 array. One function foo(n) randomly picks n of those matrices, and combine them to a big square.
If n has integer square root, then foo(n) returns a sqrt(n) * 20 by sqrt(n) * 20 matrix.
If n does not have integer square root, then foo(n) returns a ceil(sqrt(n)) * 20 by ceil(sqrt(n)) * 20 matrix. On the last row of the big square image (a row of 20 by 20 matrices), foo(n) fills ceil(sqrt(n)) ^ 2 - n extra black images (each one is represented as zeros(20,20)).
My current algorithm for foo(n) is to use a nested loop. In the inner loop, hcat() builds a layer (consisting ceil(sqrt(n)) images). In the outer loop, vcat() combines those layers.
Then dealing with hcat() and vcat() in a loop becomes complicated.
So would:
pickimage() = randn(20,20)
n = 16
m = ceil(Int, sqrt(n))
out = Matrix{Float64}(20m, 20m)
k = 0
for i in (1:m)-1
for j in (1:m)-1
out[20i + (1:20), 20j + (1:20)] .= ((k += 1) <= n) ? pickimage() : zeros(20,20)
end
end
be a relevant solution?
I have the following cell matrix, which will be used as a confusion matrix:
confusion=cell(25,25);
Then, I have two other cell arrays, on which each line contains predicted labels (array output) and another cell matrix containing the real labels (array groundtruth).
whos output
Name Size Bytes Class Attributes
output 702250x1 80943902 cell
whos groundtruth
Name Size Bytes Class Attributes
groundtruth 702250x1 84270000 cell
Then, I created the following script to create the confusion matrix
function confusion=write_confusion_matrix(predict, groundtruth)
confusion=cell(25,25);
for i=1:size(predict,1)
confusion{groundtruth{i},predict{i}}=confusion{groundtruth{i}, predict{i}}+1;
end
end
But when I run it in matlab I have the following error:
Index exceeds matrix dimensions.
Error in write_confusion_matrix (line 4)
confusion{groundtruth{i},predict{i}}=confusion{groundtruth{i}, predict{i}}+1;
I was curious to print output's and groundtruth's values to see what was happening
output{1}
ans =
2
groundtruth{1}
ans =
1
So, nothing seems to be wrong with the values, so what is wrong here? is the confusion matrix's indexing right in the code?
The error occurs in a for loop. Checking the first iteration of the loop is not sufficient in this case. Index exceeds matrix dimensions means there exists an i in the range of 1:size(output,1) for which either groundtruth{i} or output{i} is greater than 25.
You can find out which one has at least one element bigger than the range:
% 0 means no, there is none above 25. 1 means yes, there exists at least one:
hasoutlier = any(cellfun(#(x) x > 25, groundtruth)) % similar for 'output'
Or you can count them:
outliercount = sum(cellfun(#(x) x > 25, groundtruth))
Maybe you also want to find these elements:
outlierindex = find(cellfun(#(x) x > 25, groundtruth))
By the way, I am wondering why are you working with cell arrays in this case? Why not numeric arrays?
I perhaps am going about this wrong, but I have data{1}, data{2}...data{i}. Within each, I have .type1, .type2.... .typeN. The arrays are different lengths, so horizontal concatenation does not work.
For simplicity sake
>> data{1}.type1
ans =
1
2
3
>> data{2}.type1
ans =
2
4
5
6
Results should be [1;2;3;2;4;5;6]
I've been trying to loop it but not sure how? I will have a variable number of files (a,b..). How do I go about looping and concatenating? Ultimately I need a 1xN array of all of this..
My working code, thanks..figured it out..
for i = 1:Types
currentType = nTypes{i}
allData.(currentType)=[];
for j = 1:nData
allData.(currentType) = [allData.(currentType); data{j}.(currentType)(:,3)]; %3rd column
end
end
Look at cat, the first argument is the dimension. In your simple example it would be:
result = cat(1,a,b);
Which is equivalent to:
result = [a;b];
Or you can concatenate them as row vectors and transpose back to a column vector:
result = [a',b']';
For the case of a structure inside a cell array I don't think there will be any way around looping. Let's say you have a cell array with M elements and N "types" as the structure fields for each element. You could do:
M=length(data);
newData=struct;
for i=1:M
for j=1:N
field=sprintf('type%d',j); % //field name
if (M==1), newData.(field)=[]; end % //if this is a new field, create it
newData.(field)=[newData.(field);data{i}.(field)];
end
end
I have this loop which generates a vector "Diff". How do I place the values of Diff in an array that records all the Diff's generated? The problem is that the length of Diff should be a fixed length (36) which is the width of the table "CleanPrice". But because col_set varies in length (according to the number of NaNs in the data it is reading), then Diff also varies in length. What I need it to do is assign the answers generated according to their appropriate column number. i.e. row(i) of diff should contain col(i) where all other rows in Diff should be assigned a "0" or "NaN". Basically I need DiffArray to be a (nTrials x 36) array where each row is the (36 x 1) DiffArray generated. At the moment though, each time the length of col changes, I get the following error:
??? Subscripted assignment dimension mismatch.
Error in ==> NSSmodel
at 41 DiffMatrix(end+1,:)=Diff
This is my code:
DiffArray=[];
StartRow=2935;
EndRow=2940;
nTrials=EndRow-StartRow;
for row=StartRow:EndRow;
col_set=find(~isnan(gcm3.data.CleanPrice(row,1:end)));
col=col_set(:,2:end);
CleanPrices=transpose(gcm3.data.CleanPrice(row,col));
Maturity=gcm3.data.CouponandMaturity(col-1,2);
SettleDate=gcm3.data.CouponandMaturity(row,3);
Settle = repmat(SettleDate,[length(Maturity) 1]);
CleanPrices =transpose(gcm3.data.CleanPrice(row,col));
CouponRate = gcm3.data.CouponandMaturity(col-1,1);
Instruments = [Settle Maturity CleanPrices CouponRate];
PlottingPoints = gcm3.data.CouponandMaturity(1,2):gcm3.data.CouponandMaturity(36,2);
Yield = bndyield(CleanPrices,CouponRate,Settle,Maturity);
SvenssonModel = IRFunctionCurve.fitSvensson('Zero',SettleDate,Instruments)
ParYield=SvenssonModel.getParYields(Maturity);
[PriceActual, AccruedIntActual] = bndprice(Yield, CouponRate, Settle, Maturity);
[PriceNSS, AccruedIntNSS] = bndprice(ParYield, CouponRate, Settle, Maturity);
Diff=PriceActual-PriceNSS
DiffArray(end+1,:)=Diff
end
I looked at num2cell in this post but wasn't sure how to apply it correctly and started getting errors relating to that instead.
Is it correct to say you want to add an 'incomplete' row to DiffArray? If you know exactly where each element should go you could maybe do something like this:
indices = [1:7; 2:8; 3:9; [1 2 3 6 7 8 10]];
Diff = rand(4, 7);
DiffArray = zeros(4, 10) * NaN;
for row = 1:4
DiffArray(row, indices(row, :)) = Diff(row,:);
end
of course in your case you would be calculating Diff and Index (a row vector) inside the loop and not using preassigned arrays. The above is just to illustrate how to use an indexing vector to position a short row in a matrix.