Getting argmax values from 3D array
I have 3 loops and I would like to get the best value of degree and C from the mean. The
step_size = np.logspace(-2,3,6) //C values
degree = [1,2,3,4,5] //degree values
d_no =0
end_result = np.zeros((5,6,5))
for d in degree:
log_no = 0
for item in step_size:
fold_no= 0
for train_index, vld_index in kf.split(X_train):
svc_poly = SVC(kernel='poly', degree=d, C=item,probability=True)
//some codes
fold_no += 1
log_no += 1
//mean is calculated here
d_no +=1
//end for loop
The end_result should have 5x6x5 matrix in the end. Consisting of mean for every degree in every fold.
So how can I apply argmax for this 3D array?
I learned that if it is in 2d array, I can simply use for eg:
best_c = step_size[np.argmax(mean)]
print(best_c)
So how can i do something like this to get best degree and C with 3d array? Thank you in advance!
python python-3.x for-loop multidimensional-array
asked Nov 21 '18 at 9:34
EllyElly
4719
add a comment |
I have 3 loops and I would like to get the best value of degree and C from the mean. The
step_size = np.logspace(-2,3,6) //C values
degree = [1,2,3,4,5] //degree values
d_no =0
end_result = np.zeros((5,6,5))
for d in degree:
log_no = 0
for item in step_size:
fold_no= 0
for train_index, vld_index in kf.split(X_train):
svc_poly = SVC(kernel='poly', degree=d, C=item,probability=True)
//some codes
fold_no += 1
log_no += 1
//mean is calculated here
d_no +=1
//end for loop
The end_result should have 5x6x5 matrix in the end. Consisting of mean for every degree in every fold.
So how can I apply argmax for this 3D array?
I learned that if it is in 2d array, I can simply use for eg:
best_c = step_size[np.argmax(mean)]
print(best_c)
So how can i do something like this to get best degree and C with 3d array? Thank you in advance!
python python-3.x for-loop multidimensional-array
asked Nov 21 '18 at 9:34
EllyElly
4719
add a comment |
I have 3 loops and I would like to get the best value of degree and C from the mean. The
step_size = np.logspace(-2,3,6) //C values
degree = [1,2,3,4,5] //degree values
d_no =0
end_result = np.zeros((5,6,5))
for d in degree:
log_no = 0
for item in step_size:
fold_no= 0
for train_index, vld_index in kf.split(X_train):
svc_poly = SVC(kernel='poly', degree=d, C=item,probability=True)
//some codes
fold_no += 1
log_no += 1
//mean is calculated here
d_no +=1
//end for loop
The end_result should have 5x6x5 matrix in the end. Consisting of mean for every degree in every fold.
So how can I apply argmax for this 3D array?
I learned that if it is in 2d array, I can simply use for eg:
best_c = step_size[np.argmax(mean)]
print(best_c)
So how can i do something like this to get best degree and C with 3d array? Thank you in advance!
python python-3.x for-loop multidimensional-array
asked Nov 21 '18 at 9:34
EllyElly
4719
I have 3 loops and I would like to get the best value of degree and C from the mean. The
step_size = np.logspace(-2,3,6) //C values
degree = [1,2,3,4,5] //degree values
d_no =0
end_result = np.zeros((5,6,5))
for d in degree:
log_no = 0
for item in step_size:
fold_no= 0
for train_index, vld_index in kf.split(X_train):
svc_poly = SVC(kernel='poly', degree=d, C=item,probability=True)
//some codes
fold_no += 1
log_no += 1
//mean is calculated here
d_no +=1
//end for loop
The end_result should have 5x6x5 matrix in the end. Consisting of mean for every degree in every fold.
So how can I apply argmax for this 3D array?
I learned that if it is in 2d array, I can simply use for eg:
best_c = step_size[np.argmax(mean)]
print(best_c)
So how can i do something like this to get best degree and C with 3d array? Thank you in advance!
python python-3.x for-loop multidimensional-array
python python-3.x for-loop multidimensional-array
asked Nov 21 '18 at 9:34
EllyElly
4719
asked Nov 21 '18 at 9:34
EllyElly
4719
edited Nov 21 '18 at 9:39
Elly
asked Nov 21 '18 at 9:34
EllyElly
4719
asked Nov 21 '18 at 9:34
EllyElly
4719
asked Nov 21 '18 at 9:34
EllyElly
4719
4719
add a comment |
add a comment |
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