true-di | Framework Agnostic , Zero Dependency | Dependency Injection library
kandi X-RAY | true-di Summary
kandi X-RAY | true-di Summary
Framework Agnostic, Zero Dependency, Isomorphic & Minimalistic Dependency Injection Container for TypeScript and JavaScript projects
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of true-di
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QUESTION
I was following this tutorial trying to visualize my models training progress: https://deeplearning4j.konduit.ai/tuning-and-training/visualization The simple code for the server setup is:
...ANSWER
Answered 2021-Feb-23 at 12:3712:39:05.487 [vert.x-eventloop-thread-0] INFO org.deeplearning4j.ui.VertxUIServer - Deeplearning4j UI server started at: http://localhost:9000`
12:39:05.490 [main] INFO org.deeplearning4j.ui.VertxUIServer - StatsStorage instance attached to UI: InMemoryStatsStorage(uid=bd548909)
12:39:05.803 [Thread-5] INFO org.deeplearning4j.ui.VertxUIServer - Deeplearning4j UI server is auto-stopping after thread (name: main) died.
QUESTION
I keep getting the runtime warning about division. In the code below, I used the answer of a question from this forum and even imported warnings error from this.
...ANSWER
Answered 2021-Jan-19 at 19:53In [26]: def alpha_n(V):
...: with np.errstate(invalid='ignore'):
...: alph = np.where(V!= -55, 0.01*(V+55)/(1-np.exp(-0.1*(V+55))), 0
...: .1)
...: return alph
...:
In [27]: alpha_n(np.array([1,2,3,-55]))
Out[27]: array([0.56207849, 0.5719136 , 0.58176131, 0.1 ])
QUESTION
I want to find the distance of samples to the decision boundary of a trained decision trees classifier in scikit-learn. The features are all numeric and the feature space could be of any size.
I have this visualization so far for an example 2D case based on here:
...ANSWER
Answered 2020-Apr-12 at 20:59Since there can be multiple decision boundaries around a sample, I'm going to assume distance here refers to distance to nearest decision boundary.
The solution is a recursive tree traversal algorithm. Note that decision tree doesn't allow a sample to be on boundary, like e.g. SVM, each sample in feature space must belong to one of the classes. So here we will keep modifying the sample's feature in small steps, and whenever that leads to a region with a different label (than one originally assigned to the sample by trained classifier), we assume we've reached decision boundary.
In detail, like any recursive algorithm, we have two main cases to consider:
- Base case i.e. we're at a leaf node. We simply check if the current sample have different label: if so then return it, otherwise return
None. - Non leaf nodes. There are two branches, we send the sample to both. We don't modify the sample to send it to branch it would naturally take. But before sending it to the other branch, we look at the (feature, threshold) pair of the node, and modify the sample's given feature just enough to push it on the opposite side of threshold.
Complete python code:
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