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Learning-Algorithms Examples and Code Snippets
Lines of Code : 28License : Permissive (MIT License)
Copydef add_two_numbers(l1, l2):
start = ListNode(None)
# use the same linked list as result so the Space complexity will be O(1)
start.next = l1
pointer = start
transfer = 0
while (l1 is not None) or (l2 is not None) or (transfe def find_subarray(arr, k):
n = len(arr)
if n == 0:
return -1
start = 0
end = 0
current_sum = arr[0]
while end < n:
if current_sum == k:
return (start + 1, end + 1)
if current_sum < def remove_recursively(pointer, n):
if pointer is None:
return (0, None)
# go to the end and count how many are there
result = remove_recursively(pointer.next, n)
if result[0] == n:
pointer.next = result[1]
retu Community Discussions
Trending Discussions on Learning-Algorithms
QUESTION
I am running k-fold repeated training with the caret package and would like to calculate the confidence interval for my accuracy metrics. This tutorial prints a caret training object that shows accuracy/kappa metrics and associated SD: https://machinelearningmastery.com/tune-machine-learning-algorithms-in-r/. However, when I do this, all that is listed are the metric average values.
...ANSWER
Answered 2021-Mar-01 at 07:44It looks like it is stored in the results variable of the resultant object.
QUESTION
Suppose I have one or multiple tiles consisting of a single pattern (e.g. materials like: wood, concrete, gravel...) that I would like to train my classifier on, and then I'll use the trained classifier to determine to which class each pixel in another image belong.
Below are example of two tiles I would like to train the classifier on:
And let's say I want to segment the image below to identify the pixels belonging to the door and those belonging to the wall. It's just an example, I know this image isn't made of exactly the same patterns as the tiles above:
For this specific problem, is it necessary to use convolutional neural networks? Or is there a way to achieve my goal with a shallow neural network or any other classifier, combined with texture features for example?
I've already implemented a classifier with Scikit-learn which works on tile pixels individually (see code below where training_data is a vector of singletons), but I want instead to train the classifier on texture patterns.
ANSWER
Answered 2019-Nov-02 at 19:13You can use U-Net or SegNet for image segmentation. In fact you add residual layers to your CNN to get this result:
About U-Net:
Arxiv: U-Net: Convolutional Networks for Biomedical Image Segmentation
Seg-Net:
Arxiv: SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Here are Simple Examples of Codes: keras==1.1.0
U-Net:
QUESTION
While building a new neural network I seem unable to split the data. For some unknown reason it wont import train.test.split
...ImportError: cannot import name 'cross_validation'
ANSWER
Answered 2018-Oct-11 at 09:23The module has been removed since 0.20.
Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.cross_val_score instead.
QUESTION
What I need is to:
- Apply a logistic regression classifier
- Report the per-class ROC using the AUC.
- Use the estimated probabilities of the logistic regression to guide the construction of the ROC.
- 5fold cross validation for the training your model.
For this, my approach was to use this really nice tutorial:
From his idea and method I simply changed how I obtain the raw data which I am getting like this:
...ANSWER
Answered 2019-May-03 at 11:17The iris dataset is usually ordered with respect to classes. Hence, when you split without shuffling, the test dataset might get only one class.
One simple solution would be using shuffle parameter.
QUESTION
I tried to follow the example codes at https://machinelearningmastery.com/tune-machine-learning-algorithms-in-r/ but my output did not showing up accuracy and kappa sd. What am i missing? My caret library is 3.5.2 on Windows 10 Pro.
My output was:
...ANSWER
Answered 2019-Jan-29 at 12:06In the tutorial it's not specified how the output with SD's was obtained. It actually wasn't just rf_default. Instead,
QUESTION
I am looking into the time complexities of Machine Learning Algorithms and I cannot find what is the time complexity of Logistic Regression for predicting a new input. I have read that for Classification is O(c*d) c-beeing the number of classes, d-beeing the number of dimensions and I know that for the Linear Regression the search/prediction time complexity is O(d). Could you maybe explain what is the search/predict time complexity of Logistic Regression? Thank you in advance
Example For The other Machine Learning Problems: https://www.thekerneltrip.com/machine/learning/computational-complexity-learning-algorithms/
...ANSWER
Answered 2019-Jan-17 at 16:19- f - number of features (+1 because of bias). Multiplication of each feature times it's weight (
foperations,+1for bias). Anotherf + 1operations for summing all of them (obtaining prediction). Using gradient method to improve weights counts for the same number of operations, so in total we get 4* (f+1) (two for forward pass, two for backward), which is simply O(f+1). - c - number of classes (possible outputs) in your logistic regression. For binary classification it's one, so this term cancels out. Each class has it's corresponding set of weights.
- s - number of samples in your dataset, this one is quite intuitive I think.
- E - number of epochs you are willing to run the gradient descent (whole passes through dataset)
Note: this complexity can change based on things like regularization (another c operations), but the idea standing behind it goes like this.
Complexity of predictions for one sample: O((f+1)c)- f + 1 - you simply multiply each weight by the value of feature, add bias and sum all of it together in the end.
- c - you do it for every class, 1 for binary predictions.
- (f+1)c - see complexity for one sample
- s - number of samples
For multiclass logistic regression it will be softmax, while linear regression, as the name suggests, has linear activation (effectively no activation). It does not change the complexity using big O notation, but it's another c*f operations during the training (didn't want to clutter the picture further) multiplied by 2 for backprop.
QUESTION
I'm learning Random Forest. For learning purpose I'm using following link random Forest. I'm trying to run the code given in this link using my R-3.4.1. But while running the following code for missing value treatment
...ANSWER
Answered 2017-Sep-14 at 15:06The key mistake (among many mistakes) in that code was that there is no data parameter. The parameter name is obj. When I change that the example code runs.
You also need to set on= or setkey given that the object is a data.table, or simply change it to a data.frame for the imputation step:
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