--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/CSP2/CSP2_env/env-d9b9114564458d9d-741b3de822f2aaca6c6caa4325c4afce/lib/python3.8/site-packages/Bio/NaiveBayes.py	Tue Mar 18 17:55:14 2025 -0400
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+# Copyright 2000 by Jeffrey Chang.  All rights reserved.
+#
+# This file is part of the Biopython distribution and governed by your
+# choice of the "Biopython License Agreement" or the "BSD 3-Clause License".
+# Please see the LICENSE file that should have been included as part of this
+# package.
+
+"""General Naive Bayes learner (DEPRECATED).
+
+Naive Bayes is a supervised classification algorithm that uses Bayes
+rule to compute the fit between a new observation and some previously
+observed data.  The observations are discrete feature vectors, with
+the Bayes assumption that the features are independent.  Although this
+is hardly ever true, the classifier works well enough in practice.
+
+Glossary:
+ - observation - A feature vector of discrete data.
+ - class       - A possible classification for an observation.
+
+Classes:
+ - NaiveBayes - Holds information for a naive Bayes classifier.
+
+Functions:
+ - train     - Train a new naive Bayes classifier.
+ - calculate - Calculate the probabilities of each class,
+   given an observation.
+ - classify  - Classify an observation into a class.
+
+"""
+
+
+import warnings
+from Bio import BiopythonDeprecationWarning
+
+warnings.warn(
+    "The 'Bio.NaiveBayes' module is deprecated and will be removed in a future "
+    "release of Biopython. Consider using scikit-learn instead.",
+    BiopythonDeprecationWarning,
+)
+
+
+try:
+    import numpy as np
+except ImportError:
+    from Bio import MissingPythonDependencyError
+
+    raise MissingPythonDependencyError(
+        "Please install NumPy if you want to use Bio.NaiveBayes. "
+        "See http://www.numpy.org/"
+    ) from None
+
+
+def _contents(items):
+    """Return a dictionary where the key is the item and the value is the probablity associated (PRIVATE)."""
+    term = 1.0 / len(items)
+    counts = {}
+    for item in items:
+        counts[item] = counts.get(item, 0) + term
+    return counts
+
+
+class NaiveBayes:
+    """Hold information for a NaiveBayes classifier.
+
+    Attributes:
+     - classes        - List of the possible classes of data.
+     - p_conditional  - CLASS x DIM array of dicts of value -> ``P(value|class,dim)``
+     - p_prior        - List of the prior probabilities for every class.
+     - dimensionality - Dimensionality of the data.
+
+    """
+
+    def __init__(self):
+        """Initialize the class."""
+        self.classes = []
+        self.p_conditional = None
+        self.p_prior = []
+        self.dimensionality = None
+
+
+def calculate(nb, observation, scale=False):
+    """Calculate the logarithmic conditional probability for each class.
+
+    Arguments:
+     - nb          - A NaiveBayes classifier that has been trained.
+     - observation - A list representing the observed data.
+     - scale       - Boolean to indicate whether the probability should be
+       scaled by ``P(observation)``.  By default, no scaling is done.
+
+    A dictionary is returned where the key is the class and the value is
+    the log probability of the class.
+    """
+    # P(class|observation) = P(observation|class)*P(class)/P(observation)
+    # Taking the log:
+    # lP(class|observation) = lP(observation|class)+lP(class)-lP(observation)
+
+    # Make sure the observation has the right dimensionality.
+    if len(observation) != nb.dimensionality:
+        raise ValueError(
+            f"observation in {len(observation)} dimension,"
+            f" but classifier in {nb.dimensionality}"
+        )
+
+    # Calculate log P(observation|class) for every class.
+    n = len(nb.classes)
+    lp_observation_class = np.zeros(n)  # array of log P(observation|class)
+    for i in range(n):
+        # log P(observation|class) = SUM_i log P(observation_i|class)
+        probs = [None] * len(observation)
+        for j in range(len(observation)):
+            probs[j] = nb.p_conditional[i][j].get(observation[j], 0)
+        lprobs = np.log(np.clip(probs, 1.0e-300, 1.0e300))
+        lp_observation_class[i] = sum(lprobs)
+
+    # Calculate log P(class).
+    lp_prior = np.log(nb.p_prior)
+
+    # Calculate log P(observation).
+    lp_observation = 0.0  # P(observation)
+    if scale:  # Only calculate this if requested.
+        # log P(observation) = log SUM_i P(observation|class_i)P(class_i)
+        obs = np.exp(np.clip(lp_prior + lp_observation_class, -700, +700))
+        lp_observation = np.log(sum(obs))
+
+    # Calculate log P(class|observation).
+    lp_class_observation = {}  # Dict of class : log P(class|observation)
+    for i in range(len(nb.classes)):
+        lp_class_observation[nb.classes[i]] = (
+            lp_observation_class[i] + lp_prior[i] - lp_observation
+        )
+
+    return lp_class_observation
+
+
+def classify(nb, observation):
+    """Classify an observation into a class."""
+    # The class is the one with the highest probability.
+    probs = calculate(nb, observation, scale=False)
+    max_prob = max_class = None
+    for klass in nb.classes:
+        if max_prob is None or probs[klass] > max_prob:
+            max_prob, max_class = probs[klass], klass
+    return max_class
+
+
+def train(training_set, results, priors=None, typecode=None):
+    """Train a NaiveBayes classifier on a training set.
+
+    Arguments:
+     - training_set - List of observations.
+     - results      - List of the class assignments for each observation.
+       Thus, training_set and results must be the same length.
+     - priors       - Optional dictionary specifying the prior probabilities
+       for each type of result. If not specified, the priors will
+       be estimated from the training results.
+
+    """
+    if not len(training_set):
+        raise ValueError("No data in the training set.")
+    if len(training_set) != len(results):
+        raise ValueError("training_set and results should be parallel lists.")
+
+    # If no typecode is specified, try to pick a reasonable one.  If
+    # training_set is a Numeric array, then use that typecode.
+    # Otherwise, choose a reasonable default.
+    # XXX NOT IMPLEMENTED
+
+    # Check to make sure each vector in the training set has the same
+    # dimensionality.
+    dimensions = [len(x) for x in training_set]
+    if min(dimensions) != max(dimensions):
+        raise ValueError("observations have different dimensionality")
+
+    nb = NaiveBayes()
+    nb.dimensionality = dimensions[0]
+
+    # Get a list of all the classes, and
+    # estimate the prior probabilities for the classes.
+    if priors is not None:
+        percs = priors
+        nb.classes = list(set(results))
+    else:
+        class_freq = _contents(results)
+        nb.classes = list(class_freq.keys())
+        percs = class_freq
+    nb.classes.sort()  # keep it tidy
+
+    nb.p_prior = np.zeros(len(nb.classes))
+    for i in range(len(nb.classes)):
+        nb.p_prior[i] = percs[nb.classes[i]]
+
+    # Collect all the observations in class.  For each class, make a
+    # matrix of training instances versus dimensions.  I might be able
+    # to optimize this with Numeric, if the training_set parameter
+    # were guaranteed to be a matrix.  However, this may not be the
+    # case, because the client may be hacking up a sparse matrix or
+    # something.
+    c2i = {}  # class to index of class
+    for index, key in enumerate(nb.classes):
+        c2i[key] = index
+    observations = [[] for c in nb.classes]  # separate observations by class
+    for i in range(len(results)):
+        klass, obs = results[i], training_set[i]
+        observations[c2i[klass]].append(obs)
+    # Now make the observations Numeric matrix.
+    for i in range(len(observations)):
+        # XXX typecode must be specified!
+        observations[i] = np.asarray(observations[i], typecode)
+
+    # Calculate P(value|class,dim) for every class.
+    # This is a good loop to optimize.
+    nb.p_conditional = []
+    for i in range(len(nb.classes)):
+        class_observations = observations[i]  # observations for this class
+        nb.p_conditional.append([None] * nb.dimensionality)
+        for j in range(nb.dimensionality):
+            # Collect all the values in this dimension.
+            values = class_observations[:, j]
+
+            # Add pseudocounts here.  This needs to be parameterized.
+            # values = list(values) + range(len(nb.classes))  # XXX add 1
+
+            # Estimate P(value|class,dim)
+            nb.p_conditional[i][j] = _contents(values)
+    return nb