--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/CSP2/CSP2_env/env-d9b9114564458d9d-741b3de822f2aaca6c6caa4325c4afce/lib/python3.8/site-packages/Bio/MaxEntropy.py	Tue Mar 18 17:55:14 2025 -0400
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+# Copyright 2001 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.
+"""Maximum Entropy code.
+
+Uses Improved Iterative Scaling.
+"""
+# TODO Define terminology
+
+from functools import reduce
+import warnings
+
+try:
+    import numpy as np
+except ImportError:
+    from Bio import MissingPythonDependencyError
+
+    raise MissingPythonDependencyError(
+        "Please install NumPy if you want to use Bio.MaxEntropy. "
+        "See http://www.numpy.org/"
+    ) from None
+
+
+from Bio import BiopythonDeprecationWarning
+
+warnings.warn(
+    "The 'Bio.MaxEntropy' module is deprecated and will be removed in a future "
+    "release of Biopython. Consider using scikit-learn instead.",
+    BiopythonDeprecationWarning,
+)
+
+
+class MaxEntropy:
+    """Hold information for a Maximum Entropy classifier.
+
+    Members:
+    classes      List of the possible classes of data.
+    alphas       List of the weights for each feature.
+    feature_fns  List of the feature functions.
+
+    Car data from example Naive Bayes Classifier example by Eric Meisner November 22, 2003
+    http://www.inf.u-szeged.hu/~ormandi/teaching
+
+    >>> from Bio.MaxEntropy import train, classify
+    >>> xcar = [
+    ...     ['Red', 'Sports', 'Domestic'],
+    ...     ['Red', 'Sports', 'Domestic'],
+    ...     ['Red', 'Sports', 'Domestic'],
+    ...     ['Yellow', 'Sports', 'Domestic'],
+    ...     ['Yellow', 'Sports', 'Imported'],
+    ...     ['Yellow', 'SUV', 'Imported'],
+    ...     ['Yellow', 'SUV', 'Imported'],
+    ...     ['Yellow', 'SUV', 'Domestic'],
+    ...     ['Red', 'SUV', 'Imported'],
+    ...     ['Red', 'Sports', 'Imported']]
+    >>> ycar = ['Yes','No','Yes','No','Yes','No','Yes','No','No','Yes']
+
+    Requires some rules or features
+
+    >>> def udf1(ts, cl):
+    ...     return ts[0] != 'Red'
+    ...
+    >>> def udf2(ts, cl):
+    ...     return ts[1] != 'Sports'
+    ...
+    >>> def udf3(ts, cl):
+    ...     return ts[2] != 'Domestic'
+    ...
+    >>> user_functions = [udf1, udf2, udf3]  # must be an iterable type
+    >>> xe = train(xcar, ycar, user_functions)
+    >>> for xv, yv in zip(xcar, ycar):
+    ...     xc = classify(xe, xv)
+    ...     print('Pred: %s gives %s y is %s' % (xv, xc, yv))
+    ...
+    Pred: ['Red', 'Sports', 'Domestic'] gives No y is Yes
+    Pred: ['Red', 'Sports', 'Domestic'] gives No y is No
+    Pred: ['Red', 'Sports', 'Domestic'] gives No y is Yes
+    Pred: ['Yellow', 'Sports', 'Domestic'] gives No y is No
+    Pred: ['Yellow', 'Sports', 'Imported'] gives No y is Yes
+    Pred: ['Yellow', 'SUV', 'Imported'] gives No y is No
+    Pred: ['Yellow', 'SUV', 'Imported'] gives No y is Yes
+    Pred: ['Yellow', 'SUV', 'Domestic'] gives No y is No
+    Pred: ['Red', 'SUV', 'Imported'] gives No y is No
+    Pred: ['Red', 'Sports', 'Imported'] gives No y is Yes
+    """
+
+    def __init__(self):
+        """Initialize the class."""
+        self.classes = []
+        self.alphas = []
+        self.feature_fns = []
+
+
+def calculate(me, observation):
+    """Calculate the log of the probability for each class.
+
+    me is a MaxEntropy object that has been trained.  observation is a vector
+    representing the observed data.  The return value is a list of
+    unnormalized log probabilities for each class.
+    """
+    scores = []
+    assert len(me.feature_fns) == len(me.alphas)
+    for klass in me.classes:
+        lprob = 0.0
+        for fn, alpha in zip(me.feature_fns, me.alphas):
+            lprob += fn(observation, klass) * alpha
+        scores.append(lprob)
+    return scores
+
+
+def classify(me, observation):
+    """Classify an observation into a class."""
+    scores = calculate(me, observation)
+    max_score, klass = scores[0], me.classes[0]
+    for i in range(1, len(scores)):
+        if scores[i] > max_score:
+            max_score, klass = scores[i], me.classes[i]
+    return klass
+
+
+def _eval_feature_fn(fn, xs, classes):
+    """Evaluate a feature function on every instance of the training set and class (PRIVATE).
+
+    fn is a callback function that takes two parameters: a
+    training instance and a class.  Return a dictionary of (training
+    set index, class index) -> non-zero value.  Values of 0 are not
+    stored in the dictionary.
+    """
+    values = {}
+    for i in range(len(xs)):
+        for j in range(len(classes)):
+            f = fn(xs[i], classes[j])
+            if f != 0:
+                values[(i, j)] = f
+    return values
+
+
+def _calc_empirical_expects(xs, ys, classes, features):
+    """Calculate the expectation of each function from the data (PRIVATE).
+
+    This is the constraint for the maximum entropy distribution. Return a
+    list of expectations, parallel to the list of features.
+    """
+    # E[f_i] = SUM_x,y P(x, y) f(x, y)
+    #        = 1/N f(x, y)
+    class2index = {}
+    for index, key in enumerate(classes):
+        class2index[key] = index
+    ys_i = [class2index[y] for y in ys]
+
+    expect = []
+    N = len(xs)
+    for feature in features:
+        s = 0
+        for i in range(N):
+            s += feature.get((i, ys_i[i]), 0)
+        expect.append(s / N)
+    return expect
+
+
+def _calc_model_expects(xs, classes, features, alphas):
+    """Calculate the expectation of each feature from the model (PRIVATE).
+
+    This is not used in maximum entropy training, but provides a good function
+    for debugging.
+    """
+    # SUM_X P(x) SUM_Y P(Y|X) F(X, Y)
+    # = 1/N SUM_X SUM_Y P(Y|X) F(X, Y)
+    p_yx = _calc_p_class_given_x(xs, classes, features, alphas)
+
+    expects = []
+    for feature in features:
+        sum = 0.0
+        for (i, j), f in feature.items():
+            sum += p_yx[i][j] * f
+        expects.append(sum / len(xs))
+    return expects
+
+
+def _calc_p_class_given_x(xs, classes, features, alphas):
+    """Calculate conditional probability P(y|x) (PRIVATE).
+
+    y is the class and x is an instance from the training set.
+    Return a XSxCLASSES matrix of probabilities.
+    """
+    prob_yx = np.zeros((len(xs), len(classes)))
+
+    # Calculate log P(y, x).
+    assert len(features) == len(alphas)
+    for feature, alpha in zip(features, alphas):
+        for (x, y), f in feature.items():
+            prob_yx[x][y] += alpha * f
+    # Take an exponent to get P(y, x)
+    prob_yx = np.exp(prob_yx)
+    # Divide out the probability over each class, so we get P(y|x).
+    for i in range(len(xs)):
+        z = sum(prob_yx[i])
+        prob_yx[i] = prob_yx[i] / z
+    return prob_yx
+
+
+def _calc_f_sharp(N, nclasses, features):
+    """Calculate a matrix of f sharp values (PRIVATE)."""
+    # f#(x, y) = SUM_i feature(x, y)
+    f_sharp = np.zeros((N, nclasses))
+    for feature in features:
+        for (i, j), f in feature.items():
+            f_sharp[i][j] += f
+    return f_sharp
+
+
+def _iis_solve_delta(
+    N, feature, f_sharp, empirical, prob_yx, max_newton_iterations, newton_converge
+):
+    """Solve delta using Newton's method (PRIVATE)."""
+    # SUM_x P(x) * SUM_c P(c|x) f_i(x, c) e^[delta_i * f#(x, c)] = 0
+    delta = 0.0
+    iters = 0
+    while iters < max_newton_iterations:  # iterate for Newton's method
+        f_newton = df_newton = 0.0  # evaluate the function and derivative
+        for (i, j), f in feature.items():
+            prod = prob_yx[i][j] * f * np.exp(delta * f_sharp[i][j])
+            f_newton += prod
+            df_newton += prod * f_sharp[i][j]
+        f_newton, df_newton = empirical - f_newton / N, -df_newton / N
+
+        ratio = f_newton / df_newton
+        delta -= ratio
+        if np.fabs(ratio) < newton_converge:  # converged
+            break
+        iters = iters + 1
+    else:
+        raise RuntimeError("Newton's method did not converge")
+    return delta
+
+
+def _train_iis(
+    xs,
+    classes,
+    features,
+    f_sharp,
+    alphas,
+    e_empirical,
+    max_newton_iterations,
+    newton_converge,
+):
+    """Do one iteration of hill climbing to find better alphas (PRIVATE)."""
+    # This is a good function to parallelize.
+
+    # Pre-calculate P(y|x)
+    p_yx = _calc_p_class_given_x(xs, classes, features, alphas)
+
+    N = len(xs)
+    newalphas = alphas[:]
+    for i in range(len(alphas)):
+        delta = _iis_solve_delta(
+            N,
+            features[i],
+            f_sharp,
+            e_empirical[i],
+            p_yx,
+            max_newton_iterations,
+            newton_converge,
+        )
+        newalphas[i] += delta
+    return newalphas
+
+
+def train(
+    training_set,
+    results,
+    feature_fns,
+    update_fn=None,
+    max_iis_iterations=10000,
+    iis_converge=1.0e-5,
+    max_newton_iterations=100,
+    newton_converge=1.0e-10,
+):
+    """Train a maximum entropy classifier, returns MaxEntropy object.
+
+    Train a maximum entropy classifier on a training set.
+    training_set is a list of observations.  results is a list of the
+    class assignments for each observation.  feature_fns is a list of
+    the features.  These are callback functions that take an
+    observation and class and return a 1 or 0.  update_fn is a
+    callback function that is called at each training iteration.  It is
+    passed a MaxEntropy object that encapsulates the current state of
+    the training.
+
+    The maximum number of iterations and the convergence criterion for IIS
+    are given by max_iis_iterations and iis_converge, respectively, while
+    max_newton_iterations and newton_converge are the maximum number
+    of iterations and the convergence criterion for Newton's method.
+    """
+    if not 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.")
+
+    # Rename variables for convenience.
+    xs, ys = training_set, results
+
+    # Get a list of all the classes that need to be trained.
+    classes = sorted(set(results))
+
+    # Cache values for all features.
+    features = [_eval_feature_fn(fn, training_set, classes) for fn in feature_fns]
+    # Cache values for f#.
+    f_sharp = _calc_f_sharp(len(training_set), len(classes), features)
+
+    # Pre-calculate the empirical expectations of the features.
+    e_empirical = _calc_empirical_expects(xs, ys, classes, features)
+
+    # Now train the alpha parameters to weigh each feature.
+    alphas = [0.0] * len(features)
+    iters = 0
+    while iters < max_iis_iterations:
+        nalphas = _train_iis(
+            xs,
+            classes,
+            features,
+            f_sharp,
+            alphas,
+            e_empirical,
+            max_newton_iterations,
+            newton_converge,
+        )
+        diff = [np.fabs(x - y) for x, y in zip(alphas, nalphas)]
+        diff = reduce(np.add, diff, 0)
+        alphas = nalphas
+
+        me = MaxEntropy()
+        me.alphas, me.classes, me.feature_fns = alphas, classes, feature_fns
+        if update_fn is not None:
+            update_fn(me)
+
+        if diff < iis_converge:  # converged
+            break
+    else:
+        raise RuntimeError("IIS did not converge")
+
+    return me
+
+
+if __name__ == "__main__":
+    from Bio._utils import run_doctest
+
+    run_doctest(verbose=0)