<reponame>gilbertfrancois/generative-modeling
# Copyright 2020 <NAME>

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
from sklearn.mixture import BayesianGaussianMixture


def clamp(x):
    x = np.minimum(x, 1)
    x = np.maximum(x, 0)
    return x


class BayesClassifierGMM:
    def __init__(self):
        """ Constructor

        """
        # Models per class
        self.gaussian_list = []
        # Number of classes
        self.K = 0
        # probability of a given class
        self.Py = np.zeros(self.K)

    def fit(self, X, Y):
        """ Fit the model for the given training data.

        Parameters
        ----------
        X: np.array
            Data with shape (N, M) where N is number of samples and M is number
            of properties.
        Y: np.array
            Labels with shape (N,) where the labels are numbered like 0..K-1
        """
        self.K = len(set(Y))
        self.Py = np.zeros(self.K)
        for k in range(self.K):
            print(f"Fitting Gaussian Mixture Model for class {k}")
            Xk = X[Y == k]
            self.Py[k] = len(Xk)
            gmm = BayesianGaussianMixture(10)
            gmm.fit(Xk)
            self.gaussian_list.append(gmm)
        self.Py = self.Py / self.Py.sum()

    def sample_given_y(self, y):
        """ Select a gaussian for class y and sample from it.

        Parameters
        ----------
        y: int
            Class y

        Returns
        -------
        (np.array, np.array)
            Tuple of the sample and the mean of the class
        """
        sample, cluster_idx = self.gaussian_list[y].sample()
        mean = self.gaussian_list[y].means_[cluster_idx]
        return {"sample": sample, "mean": mean, "class": y, "cluster_idx": cluster_idx}

    def sample(self):
        """ Pick a random class and sample from it.

        Returns
        -------
        (np.array, np.array)
            Tuple of the sample and the mean of the class
        """
        y = np.random.choice(self.K, p=self.Py)
        return self.sample_given_y(y)

# Copyright 2020 <NAME>

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
from sklearn.mixture import BayesianGaussianMixture


def clamp(x):
    x = np.minimum(x, 1)
    x = np.maximum(x, 0)
    return x


class BayesClassifierGMM:
    def __init__(self):
        """ Constructor

        """
        # Models per class
        self.gaussian_list = []
        # Number of classes
        self.K = 0
        # probability of a given class
        self.Py = np.zeros(self.K)

    def fit(self, X, Y):
        """ Fit the model for the given training data.

        Parameters
        ----------
        X: np.array
            Data with shape (N, M) where N is number of samples and M is number
            of properties.
        Y: np.array
            Labels with shape (N,) where the labels are numbered like 0..K-1
        """
        self.K = len(set(Y))
        self.Py = np.zeros(self.K)
        for k in range(self.K):
            print(f"Fitting Gaussian Mixture Model for class {k}")
            Xk = X[Y == k]
            self.Py[k] = len(Xk)
            gmm = BayesianGaussianMixture(10)
            gmm.fit(Xk)
            self.gaussian_list.append(gmm)
        self.Py = self.Py / self.Py.sum()

    def sample_given_y(self, y):
        """ Select a gaussian for class y and sample from it.

        Parameters
        ----------
        y: int
            Class y

        Returns
        -------
        (np.array, np.array)
            Tuple of the sample and the mean of the class
        """
        sample, cluster_idx = self.gaussian_list[y].sample()
        mean = self.gaussian_list[y].means_[cluster_idx]
        return {"sample": sample, "mean": mean, "class": y, "cluster_idx": cluster_idx}

    def sample(self):
        """ Pick a random class and sample from it.

        Returns
        -------
        (np.array, np.array)
            Tuple of the sample and the mean of the class
        """
        y = np.random.choice(self.K, p=self.Py)
        return self.sample_given_y(y)
# Copyright 2020 <NAME> # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at #     http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. Constructor # Models per class # Number of classes # probability of a given class Fit the model for the given training data.          Parameters         ----------         X: np.array             Data with shape (N, M) where N is number of samples and M is number             of properties.         Y: np.array             Labels with shape (N,) where the labels are numbered like 0..K-1 Select a gaussian for class y and sample from it.          Parameters         ----------         y: int             Class y          Returns         -------         (np.array, np.array)             Tuple of the sample and the mean of the class Pick a random class and sample from it.          Returns         -------         (np.array, np.array)             Tuple of the sample and the mean of the class