I was given code in Matlab made by someone else and asked to convert to python. However, I do not know MatLab.This is the code:
for i = 1:nWind
[input(a:b,:), t(a:b,1)] = EulerMethod(A(:,:,:,i),S(:,:,i),B(:,i),n,scale(:,i),tf,options);
fprintf("%d\n",i);
for j = 1:b
vwa = generate_wind([input(j,9);input(j,10)],A(:,:,:,i),S(:,:,i),B(:,i),n,scale(:,i));
wxa(j) = vwa(1);
wya(j) = vwa(2);
end
% Pick random indexes for filtered inputs
rand_index = randi(tf/0.01-1,1,filter_size);
inputf(c:d,:) = input(a+rand_index,:);
wxf(c:d,1) = wxa(1,a+rand_index);
wyf(c:d,1) = wya(1,a+rand_index);
wzf(c:d,1) = 0;
I am confused on what [input(a:b,:), t(a:b,1)] mean and if wxf, wzf, wyf are part of the MatLab library or if it's made. Also, EulerMethod and generate_wind are seprate classes. Can someone help me convert this code to python?
The only thing I really changed so far is changing the for loop from:
for i = 1:nWind
to
for i in range(1,nWind):
There's several things to unpack here.
First, MATLAB indexing is 1-based, while Python indexing is 0-based. So, your for i = 1:nWind from MATLAB should translate to for i in range(0,nWind) in Python (with the zero optional). For nWind = 5, MATLAB would produce 1,2,3,4,5 while Python range would produce 0,1,2,3,4.
Second, wxf, wyf, and wzf are local variables. MATLAB is unique in that you can assign into specific indices at the same time variables are declared. These lines are assigning the first rows of wxa and wya (since their first index is 1) into the first columns of wxf and wyf (since their second index is 1). MATLAB will also expand an array if you assign past its end.
Without seeing the rest of the code, I don't really know what c and d are doing. If c is initialized to 1 before the loop and there's something like c = d+1; later, then it would be that your variables wxf, wyf, and wzf are being initialized on the first iteration of the loop and expanded on later iterations. This is a common (if frowned upon) pattern in MATLAB. If this is the case, you'd replicate it in Python by initializing to an empty array before the loop and using the array's extend() method inside the loop (though I bet it's frowned upon in Python, as well). But really, we need you to edit your question to include a, b, c, and d if you want to be sure this is really the case.
Third, EulerMethod and generate_wind are functions, not classes. EulerMethod returns two outputs, which you'd probably replicate in Python by returning a tuple.
[input(a:b,:), t(a:b,1)] = EulerMethod(...); is assigning the two outputs of EulerMethod into specific ranges of input and t. Similar concepts as in points 1 and 2 apply here.
Those are the answers to what you expressed confusion about. Without sitting down and doing it myself, I don't have enough experience in Python to give more Python-specific recommendations.
Related
I want to assign values to part of an array which is specified by multiple conditions.
For example:
import numpy as np
temp = np.arange(120).reshape((2,3,4,5))
mask = temp > 22
submask = temp[mask] < 43
(temp[mask])[submask] = 0 # assign zeros to the part of the array specified via mask and submask
print(temp) # notice that temp is unchanged - I want it to be changed
This example is artificial. Generally I want to do something more complex which involves a combination of indexing and boolean masks. Using a list index fails in similar circumstances. For example: temp[:,:,0,[1,3,2]]=0 is a valid assignment, but temp[:,:,0,[1,3,2]][mask]=0 will fail.
My understanding is that the assignment is failing because the complex indexing is prompting numpy to make a copy of the array object and assigning to that, rather than to the original array. So it isn't that the assignment is failing per se, just that the assignment is directed towards the "wrong" object.
I have tried using functions such as np.copyto and np.putmask but these also fail, presumably because the backend implementation mimics the original problem. For example: np.putmask(temp, mask, 0) will work but np.putmask(temp[mask], submask, 0) will not.
Is there a good way to do what I want to do?
I have some Python experience, but (obviously) not an overwhelming wealth of knowledge. In short, I need to create validation arrays for Neural Network testing using 1/5th of a main array. So, I am able to append i % 5 == 0 values to a test array. From there, I can still print every 5th value. Now, when I go to remove values, I get the out of index or range error; I know it is because the remove is changing the overall value of len(string_arr). However, I have been unsuccessful in figuring out a way to compensate.
Below is a 'dummy' program to solve what I need, but not the actual thing I am working on. I need 1/5th on a validation array, remove that 1/5th from the main array, and have 4/5th left on the main to train on. Below, I tried to appended to another array and remove those values in order to not mess up the len(string_arr)... did not work.
Thank you
english_list = open('file')
for word in english_list.readlines():
word = word[:-1].lower()
if len(word) == 6:
string_arr.append(word)
target_arr.append(0)
print(string_arr)
print(len(string_arr))
for i in range(len(string_arr)):
if i % 5 == 0:
test_arr.append(string_arr[i])
for i in range(len(string_arr)):
if i % 5 == 0:
one_more.append(string_arr[i])
for i in range(len(string_arr)):
if one_more[i] == string_arr:
string_arr.remove(one_more[i])
print(test_arr)
print(len(test_arr))
print(string_arr)
print(len(string_arr))
You can do it in a slightly more compact manner with fancy list indexing:
small_cut = string_arr[::5]
remaining = [j for sub in zip(string_arr[1::5], string_arr[2::5], string_arr[3::5], string_arr[4::5]) for j in sub]
This is assuming you're working with normal python lists; if you're working with something like a numPy array, there might be even easier approaches (potentially more efficient, too) to do what you want.
This is more of a Matlab programming question than it is a math question.
I'd like to run gradient descent multiple on different learning rates. I have a set of learning rates
alpha = [0.3, 0.1, 0.03, 0.01, 0.003, 0.001];
and each time I run gradient descent, I get a vector J_vals as output. However, I don't know Matlab well enough to know how to implement this besides doing something like:
[theta, J_vals] = gradientDescent(...., alpha(1),...);
J1 = J_vals;
[theta, J_vals] = gradientDescent(...., alpha(2),...);
J2 = J_vals;
and so on.
I thought about using a for loop, but then I don't know how I would deal with the J_vals's (not sure how to apply the for loop to J1, J2, and so on). Perhaps it would look something like this:
for i = len(alpha)
[theta, J_vals] = gradientDescent(..., alpha(i),...);
J(i) = J_vals;
end
Then I would have a vector of vectors.
In Python, I would just run a for loop and append each new result to the end of a list. How do I implement something like this in Matlab? Or is there a more efficient way?
If you know how many loops you are going have and the size of the J_vals (or at least a reasonable upper bound) I would suggest pre-allocating the size of the container array
J = zeros(n,1);
then on each loop insert the new values
J(start:start+n) = J_vals
That way you don't reallocate memory. If you don't know, you can append the values to the array. For example,
J = []; % initialize
for i = len(alpha)
[theta, J_vals] = gradientDescent(..., alpha(i),...);
J = [J; J_vals]; % Append column row
end
but this is re-allocating the size of the array every loop. If it's not too many loops then it should be ok.
Matlab's "cell arrays" are kind of like lists in Python. They are similar in that you can put variable datatypes into them. Nobody seems to be too sure, but most likely the cell array is implemented as an array of object pointers. That means that it is still somewhat expensive to append to it (cell_array{length(cell_array) + 1} = new_data), but at least you are only appending a pointer instead of the entire column. You would still have to convert the cell array to a normal matrix afterward using cell2mat.
The most idiomatic Matlab solution is to pre-allocate (as #dpmcmlxxvi suggested).
I think what you are describing is a really common use case, and it's unfortunate that Matlab requires such a verbose idiom for this. Also it's frustrating that the documentation is opaque on how cell arrays are implemented and whether it is expensive to append to a cell array.
Your solution works just fine as long as you add a : for the row subscript (assuming J_vals is a column vector):
for i = len(alpha)
[theta, J_vals] = gradientDescent(..., alpha(i),...);
J(:, i) = J_vals;
%// ^... all rows, column 'i'
end
You could even put that as the return value:
for i = len(alpha)
[theta, J(:, i)] = gradientDescent(..., alpha(i),...);
%// ^... add returned value directly to our list
end
Both of these methods allow you to preallocate your matrix for a potential speed gain.
If you want to build your list as you go, you can use the method in #dpmcmlxxvi's answer, or you can use the special subscript end. Neither of these methods are compatible with preallocation, though.
for i = len(alpha)
[theta, J(:, end+1)] = gradientDescent(..., alpha(i),...);
%// ^... add new vector after the current end of list
end
I would also like to suggest you not use i as a variable name in Matlab. I know it's natural for other languages, but in Matlab it overwrites the built-in imaginary constant i.
See: https://stackoverflow.com/a/14790765/1377097
Ok, I'm in the process of learning Python, and had a quick question about for loops. I was wondering if you could use math operators in them, like JavaScript. For example, could I do:
for i = 0, i < 5, i++:
#code here
Now, I'm quite aware that Python doesn't support i++, and I think it doesn't support the commas either. So if I can do it that way, could you provide a sample.
Thanks
You would use a range loop:
for i in range(5):
#code here
If you want to increment in a loop you would use a while loop:
i = 0
while i < 5:
i += 1
To decrement you would use i -= 1.
Just as a loop is introduced by for, does not imply the same behaviour for different languages.
Python's for loop iterates over objects. Something like the C-for loop does not exist.
The C for loop (for ( <init> ; <cond> ; <update> ) <statement>, however, is actually identical to the C code:
<init>;
while ( <cond> ) {
<statement>
<update>
}
So, with the additional information that Python does have a while loop which behaves like the C-while loop, you should now be able to implement something like the C for loop in Python. I'll leave that as an exercise:-)
Note: as generating an evenly spaced sequence of integer values is a common case, Python provides the range() (Python 3) or xrange() (Python 2) function. This does create a RangeObject which (basically) yields the next value for a sequence given by start, stop and step arguments.
Quick answer
You may use:
for i in range(5):
# code here
or
i = 0
while i < 5:
i = i + 1 # or i += 1
Boring/pedantic answer
When I was learning Python I disliked the syntax; why should a simple for loop require a second keyword, range? The answer, I believe, is due to the fundamental role of the list in Python's prescriptive syntax. Repeated annoyances by range made me think about how the data were described (or not) before the loop, which in turn led me to think more Pythonically about the design of the data.
Let's say you want to populate a list with the first five perfect squares. You could:
squares = []
for i in range(5):
squares.append(i**2)
Alternatively, you could use comprehension:
initial_values = range(5) # we've declared the initial values
squares = [i**2 for i in initial_values]
Or more compactly:
squares = [i**2 for i in range(5)]
I routinely encounter problems where there's no Pythonic way to write the code, and I end up writing C-like Python (as in the Quick answer above). But just as often I find there's a more elegant and readable way to do things, and usually this indicates some imperfections in the antecedent data design.
I have a NumPy array, let's call it A. The behavior of the following code is not clear to me:
b = A[3,:];
A[3,:] = 0;
The third row must be saved in b, but surprisingly b becomes zero also!
Any Help?
To make a copy, you must, in fact, make a copy!
b = A[3,:].copy()
(By the way, python is not C: no semicolons after statements needed.)
Migrating from MATLAB/Octave I see :-)?
1) Remember Python uses 0-based arrays as opposed to MATLAB's 1-based arrays. A[3,:] will actually give you the 4th row.
2) Unlike MATLAB, taking a slice from an array like this stores the memory location of the slice from the slicee into the slicer. Hence, as Andrew J. says, you need to copy.
Based on your statement that you want "The third row must be saved in b", your code should be
b = A[2,:].copy()