Mohamad Amin Mohamadi mohamad-amin

## wide_resnet_jax.py
from functools import partial
#from typing import Callable, Optional, Sequence, Tuple
from typing import (Any, Callable, Dict, Iterable, Mapping, Optional, Sequence, Tuple,
                    Union)

import jax
from jax import lax
import jax.numpy as jnp
from flax import linen as nn

## madelon_pca_classification.ipynb

      
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                mohamad-amin
                / madelon_pca_classification.ipynb
            
            
              Created
              May 2, 2019 17:02
            
              
                Classification on Madelon dataset, using PCA feature selection
              
          
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## slow_computation_gaussian_mixture.py
# Compute the probability of each component in the mixture
def _computeProbabilities(self, limits):
    probabilities = np.zeros(numComponents) # numComponents refers to the number of components in this mixture
    for component in range(numComponents):
		# gmm is a GaussianMixture from sklearn.mixture
        mean = gmm.means_[component]
        cov = gmm.covariances_[component]
        weight = gmm.weights_[component]
        probabilities[component] = constrainedGaussianDensity(mean, cov, limits) * weight
    return probabilities

## Main.java
public class Main {

    public static void main(String[] args) {
        for (int i=0; i<10; i++) {
            SimplePresenter presenter = new SimplePresenter();
        }
    }

}
	from functools import partial
	#from typing import Callable, Optional, Sequence, Tuple
	from typing import (Any, Callable, Dict, Iterable, Mapping, Optional, Sequence, Tuple,
	Union)

	import jax
	from jax import lax
	import jax.numpy as jnp
	from flax import linen as nn
	# Compute the probability of each component in the mixture
	def _computeProbabilities(self, limits):
	probabilities = np.zeros(numComponents) # numComponents refers to the number of components in this mixture
	for component in range(numComponents):
	# gmm is a GaussianMixture from sklearn.mixture
	mean = gmm.means_[component]
	cov = gmm.covariances_[component]
	weight = gmm.weights_[component]
	probabilities[component] = constrainedGaussianDensity(mean, cov, limits) * weight
	return probabilities
	public class Main {

	public static void main(String[] args) {
	for (int i=0; i<10; i++) {
	SimplePresenter presenter = new SimplePresenter();
	}
	}

	}