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I would like to calculate this sum
using numpy.sum - how can I do that?
Phi is a function that takes one parameter, y and x are vectors of length i
Start with x and y as numpy arrays
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
You will pass in the array,
r = y - (theta_0*x + theta_1)
Define your function, and hopefully you can do vectorized operations within the function, but whatever, the function needs to return a numpy vector if you want to use numpy.sum
def Phi(r):
a = r*r # and example operation
return a # returning a numpy array
Then call the function and sum:
R = Phi(r).sum()
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I have this code in MATLAB and I am trying to convert it in Python.
A=[1,-0.75,0.25]
yc(1:45)=-2;
y(1:6)=0
u(1:6)=0
[lig,col]=size(A);
alpha(1)=1;
alpha(2)=A(2)-1;
if(col>2)
for i=3:col
alpha(i)=A(i)-A(i-1);
end ;
end;
alpha(col+1)=-A(col);
I don't know how to convert it in python thnx for helping me
It would be better if your code would have been a minimal example of what you are trying to do. You are defining variables that are not even used. But here's a more or less literal translation. Note that you probably want to preallocate alpha (both in Matlab and Python)
import numpy as np
A = np.array([1.0, -.75, .25])
yc = -2 * np.ones(45)
y = np.zeros(6)
u = np.zeros(6)
col = A.size
alpha = np.array([1, A[1] - 1])
if col > 2:
for i in range(2, col):
alpha = np.append(alpha, A[i] - A[i-1])
alpha = np.append(alpha, -A[col-1])
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I have a array, where i would like to multiply the elements inside the array by themselves (product) and i have the following vector to be multiplied by the input vector: test_vector = array([0.1, 0.3, 0.4, 0.5, 0.6).
I am looking for an easy way to automate this task
NumPy solution:
import numpy as np
arr_1 = np.array([1, 0, 1, 0, 1])
arr_2 = np.array([0.42, 0.53, 0.62, 0.60, 0.69])
res = np.prod(np.where(arr_1, arr_2, 1 - arr_2))
print(res)
Output:
0.033779088
There might be a more efficient way.
I'm not quite sure what you're calling that first array. I'm calling it signs.
result = 1
for sign, value in zip(signs, test_vector):
result *= (value if sign == 0 else 1 - value)
If these vectors are long rather than short examples as given here, you might want to switch to using numpy.
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For generating a probability density function of some cases, maybe 1 million observations are considered. When I work with numpy array, I was encountered by size limit 32.
Is it too few ?
In this case, how can we store more than 32 elements without distributing the elements into different columns and maybe arrays in arrays ?
import numpy
my_list = []
for i in range(0, 100):
my_list.append(i)
np_arr = numpy.ndarray(np_arr) # ValueError: sequence too large; cannot be greater than 32
When you create an array with numpy.ndarray, the first argument is the shape of the array. Interpreting that list as a shape would indeed give a huge array. If you just want to turn the list into an array, you want numpy.array:
import numpy
my_list = []
for i in range(0, 100):
my_list.append(i)
np_arr = numpy.array(my_list)
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sparse
array objects will have a fixed size n set when they are created - attempting to set or get elements larger
than the size of the array should raise an IndexError
Use Scipy sparse Matrics e.g. COO sparse matrix
matrix = sparse.coo_matrix((C,(A,B)),shape=(5,5))
Or you can use Pandas sparseArray :
arr = np.random.randn(10)
arr[2:5] = np.nan; arr[7:8] = np.nan
sparr = pd.SparseArray(arr)
I'd bet these are already implemented in numpy or scipy.
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I have multiple 2D arrays, three of which are LONG, LAT and HEIGHT. I would like to determine the closest index in these 2D arrays for a given (long,lat).
So, within my 2D HEIGHT array, what is the index of (-43.5,45)?
A few foolish minutes later; I have worked out an answer I believe sufficient:
a = abs( LAT-chosen_lat ) + abs( LONG-chosen_lon )
i,j = np_unravel_index(a.argmin(), a.shape)