--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/CSP2/CSP2_env/env-d9b9114564458d9d-741b3de822f2aaca6c6caa4325c4afce/lib/python3.8/site-packages/Bio/MarkovModel.py	Tue Mar 18 17:55:14 2025 -0400
@@ -0,0 +1,679 @@
+# Copyright 2002 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.
+"""A state-emitting MarkovModel.
+
+Note terminology similar to Manning and Schutze is used.
+
+
+Functions:
+train_bw        Train a markov model using the Baum-Welch algorithm.
+train_visible   Train a visible markov model using MLE.
+find_states     Find the a state sequence that explains some observations.
+
+load            Load a MarkovModel.
+save            Save a MarkovModel.
+
+Classes:
+MarkovModel     Holds the description of a markov model
+"""
+
+import warnings
+from Bio import BiopythonDeprecationWarning
+
+warnings.warn(
+    "The 'Bio.MarkovModel' module is deprecated and will be removed in a "
+    "future release of Biopython. Consider using the hmmlearn package instead.",
+    BiopythonDeprecationWarning,
+)
+
+
+try:
+    import numpy as np
+except ImportError:
+    from Bio import MissingPythonDependencyError
+
+    raise MissingPythonDependencyError(
+        "Please install NumPy if you want to use Bio.MarkovModel. "
+        "See http://www.numpy.org/"
+    ) from None
+
+logaddexp = np.logaddexp
+
+
+def itemindex(values):
+    """Return a dictionary of values with their sequence offset as keys."""
+    d = {}
+    entries = enumerate(values[::-1])
+    n = len(values) - 1
+    for index, key in entries:
+        d[key] = n - index
+    return d
+
+
+np.random.seed()
+
+VERY_SMALL_NUMBER = 1e-300
+LOG0 = np.log(VERY_SMALL_NUMBER)
+
+
+class MarkovModel:
+    """Create a state-emitting MarkovModel object."""
+
+    def __init__(
+        self, states, alphabet, p_initial=None, p_transition=None, p_emission=None
+    ):
+        """Initialize the class."""
+        self.states = states
+        self.alphabet = alphabet
+        self.p_initial = p_initial
+        self.p_transition = p_transition
+        self.p_emission = p_emission
+
+    def __str__(self):
+        """Create a string representation of the MarkovModel object."""
+        from io import StringIO
+
+        handle = StringIO()
+        save(self, handle)
+        handle.seek(0)
+        return handle.read()
+
+
+def _readline_and_check_start(handle, start):
+    """Read the first line and evaluate that begisn with the correct start (PRIVATE)."""
+    line = handle.readline()
+    if not line.startswith(start):
+        raise ValueError(f"I expected {start!r} but got {line!r}")
+    return line
+
+
+def load(handle):
+    """Parse a file handle into a MarkovModel object."""
+    # Load the states.
+    line = _readline_and_check_start(handle, "STATES:")
+    states = line.split()[1:]
+
+    # Load the alphabet.
+    line = _readline_and_check_start(handle, "ALPHABET:")
+    alphabet = line.split()[1:]
+
+    mm = MarkovModel(states, alphabet)
+    N, M = len(states), len(alphabet)
+
+    # Load the initial probabilities.
+    mm.p_initial = np.zeros(N)
+    line = _readline_and_check_start(handle, "INITIAL:")
+    for i in range(len(states)):
+        line = _readline_and_check_start(handle, f"  {states[i]}:")
+        mm.p_initial[i] = float(line.split()[-1])
+
+    # Load the transition.
+    mm.p_transition = np.zeros((N, N))
+    line = _readline_and_check_start(handle, "TRANSITION:")
+    for i in range(len(states)):
+        line = _readline_and_check_start(handle, f"  {states[i]}:")
+        mm.p_transition[i, :] = [float(v) for v in line.split()[1:]]
+
+    # Load the emission.
+    mm.p_emission = np.zeros((N, M))
+    line = _readline_and_check_start(handle, "EMISSION:")
+    for i in range(len(states)):
+        line = _readline_and_check_start(handle, f"  {states[i]}:")
+        mm.p_emission[i, :] = [float(v) for v in line.split()[1:]]
+
+    return mm
+
+
+def save(mm, handle):
+    """Save MarkovModel object into handle."""
+    # This will fail if there are spaces in the states or alphabet.
+    w = handle.write
+    w(f"STATES: {' '.join(mm.states)}\n")
+    w(f"ALPHABET: {' '.join(mm.alphabet)}\n")
+    w("INITIAL:\n")
+    for i in range(len(mm.p_initial)):
+        w(f"  {mm.states[i]}: {mm.p_initial[i]:g}\n")
+    w("TRANSITION:\n")
+    for i in range(len(mm.p_transition)):
+        w(f"  {mm.states[i]}: {' '.join(str(x) for x in mm.p_transition[i])}\n")
+    w("EMISSION:\n")
+    for i in range(len(mm.p_emission)):
+        w(f"  {mm.states[i]}: {' '.join(str(x) for x in mm.p_emission[i])}\n")
+
+
+# XXX allow them to specify starting points
+def train_bw(
+    states,
+    alphabet,
+    training_data,
+    pseudo_initial=None,
+    pseudo_transition=None,
+    pseudo_emission=None,
+    update_fn=None,
+):
+    """Train a MarkovModel using the Baum-Welch algorithm.
+
+    Train a MarkovModel using the Baum-Welch algorithm.  states is a list
+    of strings that describe the names of each state.  alphabet is a
+    list of objects that indicate the allowed outputs.  training_data
+    is a list of observations.  Each observation is a list of objects
+    from the alphabet.
+
+    pseudo_initial, pseudo_transition, and pseudo_emission are
+    optional parameters that you can use to assign pseudo-counts to
+    different matrices.  They should be matrices of the appropriate
+    size that contain numbers to add to each parameter matrix, before
+    normalization.
+
+    update_fn is an optional callback that takes parameters
+    (iteration, log_likelihood).  It is called once per iteration.
+    """
+    N, M = len(states), len(alphabet)
+    if not training_data:
+        raise ValueError("No training data given.")
+    if pseudo_initial is not None:
+        pseudo_initial = np.asarray(pseudo_initial)
+        if pseudo_initial.shape != (N,):
+            raise ValueError("pseudo_initial not shape len(states)")
+    if pseudo_transition is not None:
+        pseudo_transition = np.asarray(pseudo_transition)
+        if pseudo_transition.shape != (N, N):
+            raise ValueError("pseudo_transition not shape len(states) X len(states)")
+    if pseudo_emission is not None:
+        pseudo_emission = np.asarray(pseudo_emission)
+        if pseudo_emission.shape != (N, M):
+            raise ValueError("pseudo_emission not shape len(states) X len(alphabet)")
+
+    # Training data is given as a list of members of the alphabet.
+    # Replace those with indexes into the alphabet list for easier
+    # computation.
+    training_outputs = []
+    indexes = itemindex(alphabet)
+    for outputs in training_data:
+        training_outputs.append([indexes[x] for x in outputs])
+
+    # Do some sanity checking on the outputs.
+    lengths = [len(x) for x in training_outputs]
+    if min(lengths) == 0:
+        raise ValueError("I got training data with outputs of length 0")
+
+    # Do the training with baum welch.
+    x = _baum_welch(
+        N,
+        M,
+        training_outputs,
+        pseudo_initial=pseudo_initial,
+        pseudo_transition=pseudo_transition,
+        pseudo_emission=pseudo_emission,
+        update_fn=update_fn,
+    )
+    p_initial, p_transition, p_emission = x
+    return MarkovModel(states, alphabet, p_initial, p_transition, p_emission)
+
+
+MAX_ITERATIONS = 1000
+
+
+def _baum_welch(
+    N,
+    M,
+    training_outputs,
+    p_initial=None,
+    p_transition=None,
+    p_emission=None,
+    pseudo_initial=None,
+    pseudo_transition=None,
+    pseudo_emission=None,
+    update_fn=None,
+):
+    """Implement the Baum-Welch algorithm to evaluate unknown parameters in the MarkovModel object (PRIVATE)."""
+    if p_initial is None:
+        p_initial = _random_norm(N)
+    else:
+        p_initial = _copy_and_check(p_initial, (N,))
+
+    if p_transition is None:
+        p_transition = _random_norm((N, N))
+    else:
+        p_transition = _copy_and_check(p_transition, (N, N))
+    if p_emission is None:
+        p_emission = _random_norm((N, M))
+    else:
+        p_emission = _copy_and_check(p_emission, (N, M))
+
+    # Do all the calculations in log space to avoid underflows.
+    lp_initial = np.log(p_initial)
+    lp_transition = np.log(p_transition)
+    lp_emission = np.log(p_emission)
+    if pseudo_initial is not None:
+        lpseudo_initial = np.log(pseudo_initial)
+    else:
+        lpseudo_initial = None
+    if pseudo_transition is not None:
+        lpseudo_transition = np.log(pseudo_transition)
+    else:
+        lpseudo_transition = None
+    if pseudo_emission is not None:
+        lpseudo_emission = np.log(pseudo_emission)
+    else:
+        lpseudo_emission = None
+
+    # Iterate through each sequence of output, updating the parameters
+    # to the HMM.  Stop when the log likelihoods of the sequences
+    # stops varying.
+    prev_llik = None
+    for i in range(MAX_ITERATIONS):
+        llik = LOG0
+        for outputs in training_outputs:
+            llik += _baum_welch_one(
+                N,
+                M,
+                outputs,
+                lp_initial,
+                lp_transition,
+                lp_emission,
+                lpseudo_initial,
+                lpseudo_transition,
+                lpseudo_emission,
+            )
+        if update_fn is not None:
+            update_fn(i, llik)
+        if prev_llik is not None and np.fabs(prev_llik - llik) < 0.1:
+            break
+        prev_llik = llik
+    else:
+        raise RuntimeError("HMM did not converge in %d iterations" % MAX_ITERATIONS)
+
+    # Return everything back in normal space.
+    return [np.exp(_) for _ in (lp_initial, lp_transition, lp_emission)]
+
+
+def _baum_welch_one(
+    N,
+    M,
+    outputs,
+    lp_initial,
+    lp_transition,
+    lp_emission,
+    lpseudo_initial,
+    lpseudo_transition,
+    lpseudo_emission,
+):
+    """Execute one step for Baum-Welch algorithm (PRIVATE).
+
+    Do one iteration of Baum-Welch based on a sequence of output.
+    Changes the value for lp_initial, lp_transition and lp_emission in place.
+    """
+    T = len(outputs)
+    fmat = _forward(N, T, lp_initial, lp_transition, lp_emission, outputs)
+    bmat = _backward(N, T, lp_transition, lp_emission, outputs)
+
+    # Calculate the probability of traversing each arc for any given
+    # transition.
+    lp_arc = np.zeros((N, N, T))
+    for t in range(T):
+        k = outputs[t]
+        lp_traverse = np.zeros((N, N))  # P going over one arc.
+        for i in range(N):
+            for j in range(N):
+                # P(getting to this arc)
+                # P(making this transition)
+                # P(emitting this character)
+                # P(going to the end)
+                lp = (
+                    fmat[i][t]
+                    + lp_transition[i][j]
+                    + lp_emission[i][k]
+                    + bmat[j][t + 1]
+                )
+                lp_traverse[i][j] = lp
+        # Normalize the probability for this time step.
+        lp_arc[:, :, t] = lp_traverse - _logsum(lp_traverse)
+
+    # Sum of all the transitions out of state i at time t.
+    lp_arcout_t = np.zeros((N, T))
+    for t in range(T):
+        for i in range(N):
+            lp_arcout_t[i][t] = _logsum(lp_arc[i, :, t])
+
+    # Sum of all the transitions out of state i.
+    lp_arcout = np.zeros(N)
+    for i in range(N):
+        lp_arcout[i] = _logsum(lp_arcout_t[i, :])
+
+    # UPDATE P_INITIAL.
+    lp_initial = lp_arcout_t[:, 0]
+    if lpseudo_initial is not None:
+        lp_initial = _logvecadd(lp_initial, lpseudo_initial)
+        lp_initial = lp_initial - _logsum(lp_initial)
+
+    # UPDATE P_TRANSITION.  p_transition[i][j] is the sum of all the
+    # transitions from i to j, normalized by the sum of the
+    # transitions out of i.
+    for i in range(N):
+        for j in range(N):
+            lp_transition[i][j] = _logsum(lp_arc[i, j, :]) - lp_arcout[i]
+        if lpseudo_transition is not None:
+            lp_transition[i] = _logvecadd(lp_transition[i], lpseudo_transition)
+            lp_transition[i] = lp_transition[i] - _logsum(lp_transition[i])
+
+    # UPDATE P_EMISSION.  lp_emission[i][k] is the sum of all the
+    # transitions out of i when k is observed, divided by the sum of
+    # the transitions out of i.
+    for i in range(N):
+        ksum = np.zeros(M) + LOG0  # ksum[k] is the sum of all i with k.
+        for t in range(T):
+            k = outputs[t]
+            for j in range(N):
+                ksum[k] = logaddexp(ksum[k], lp_arc[i, j, t])
+        ksum = ksum - _logsum(ksum)  # Normalize
+        if lpseudo_emission is not None:
+            ksum = _logvecadd(ksum, lpseudo_emission[i])
+            ksum = ksum - _logsum(ksum)  # Renormalize
+        lp_emission[i, :] = ksum
+
+    # Calculate the log likelihood of the output based on the forward
+    # matrix.  Since the parameters of the HMM has changed, the log
+    # likelihoods are going to be a step behind, and we might be doing
+    # one extra iteration of training.  The alternative is to rerun
+    # the _forward algorithm and calculate from the clean one, but
+    # that may be more expensive than overshooting the training by one
+    # step.
+    return _logsum(fmat[:, T])
+
+
+def _forward(N, T, lp_initial, lp_transition, lp_emission, outputs):
+    """Implement forward algorithm (PRIVATE).
+
+    Calculate a Nx(T+1) matrix, where the last column is the total
+    probability of the output.
+    """
+    matrix = np.zeros((N, T + 1))
+
+    # Initialize the first column to be the initial values.
+    matrix[:, 0] = lp_initial
+    for t in range(1, T + 1):
+        k = outputs[t - 1]
+        for j in range(N):
+            # The probability of the state is the sum of the
+            # transitions from all the states from time t-1.
+            lprob = LOG0
+            for i in range(N):
+                lp = matrix[i][t - 1] + lp_transition[i][j] + lp_emission[i][k]
+                lprob = logaddexp(lprob, lp)
+            matrix[j][t] = lprob
+    return matrix
+
+
+def _backward(N, T, lp_transition, lp_emission, outputs):
+    """Implement backward algorithm (PRIVATE)."""
+    matrix = np.zeros((N, T + 1))
+    for t in range(T - 1, -1, -1):
+        k = outputs[t]
+        for i in range(N):
+            # The probability of the state is the sum of the
+            # transitions from all the states from time t+1.
+            lprob = LOG0
+            for j in range(N):
+                lp = matrix[j][t + 1] + lp_transition[i][j] + lp_emission[i][k]
+                lprob = logaddexp(lprob, lp)
+            matrix[i][t] = lprob
+    return matrix
+
+
+def train_visible(
+    states,
+    alphabet,
+    training_data,
+    pseudo_initial=None,
+    pseudo_transition=None,
+    pseudo_emission=None,
+):
+    """Train a visible MarkovModel using maximum likelihoood estimates for each of the parameters.
+
+    Train a visible MarkovModel using maximum likelihoood estimates
+    for each of the parameters.  states is a list of strings that
+    describe the names of each state.  alphabet is a list of objects
+    that indicate the allowed outputs.  training_data is a list of
+    (outputs, observed states) where outputs is a list of the emission
+    from the alphabet, and observed states is a list of states from
+    states.
+
+    pseudo_initial, pseudo_transition, and pseudo_emission are
+    optional parameters that you can use to assign pseudo-counts to
+    different matrices.  They should be matrices of the appropriate
+    size that contain numbers to add to each parameter matrix.
+    """
+    N, M = len(states), len(alphabet)
+    if pseudo_initial is not None:
+        pseudo_initial = np.asarray(pseudo_initial)
+        if pseudo_initial.shape != (N,):
+            raise ValueError("pseudo_initial not shape len(states)")
+    if pseudo_transition is not None:
+        pseudo_transition = np.asarray(pseudo_transition)
+        if pseudo_transition.shape != (N, N):
+            raise ValueError("pseudo_transition not shape len(states) X len(states)")
+    if pseudo_emission is not None:
+        pseudo_emission = np.asarray(pseudo_emission)
+        if pseudo_emission.shape != (N, M):
+            raise ValueError("pseudo_emission not shape len(states) X len(alphabet)")
+
+    # Training data is given as a list of members of the alphabet.
+    # Replace those with indexes into the alphabet list for easier
+    # computation.
+    training_states, training_outputs = [], []
+    states_indexes = itemindex(states)
+    outputs_indexes = itemindex(alphabet)
+    for toutputs, tstates in training_data:
+        if len(tstates) != len(toutputs):
+            raise ValueError("states and outputs not aligned")
+        training_states.append([states_indexes[x] for x in tstates])
+        training_outputs.append([outputs_indexes[x] for x in toutputs])
+
+    x = _mle(
+        N,
+        M,
+        training_outputs,
+        training_states,
+        pseudo_initial,
+        pseudo_transition,
+        pseudo_emission,
+    )
+    p_initial, p_transition, p_emission = x
+
+    return MarkovModel(states, alphabet, p_initial, p_transition, p_emission)
+
+
+def _mle(
+    N,
+    M,
+    training_outputs,
+    training_states,
+    pseudo_initial,
+    pseudo_transition,
+    pseudo_emission,
+):
+    """Implement Maximum likelihood estimation algorithm (PRIVATE)."""
+    # p_initial is the probability that a sequence of states starts
+    # off with a particular one.
+    p_initial = np.zeros(N)
+    if pseudo_initial:
+        p_initial = p_initial + pseudo_initial
+    for states in training_states:
+        p_initial[states[0]] += 1
+    p_initial = _normalize(p_initial)
+
+    # p_transition is the probability that a state leads to the next
+    # one.  C(i,j)/C(i) where i and j are states.
+    p_transition = np.zeros((N, N))
+    if pseudo_transition:
+        p_transition = p_transition + pseudo_transition
+    for states in training_states:
+        for n in range(len(states) - 1):
+            i, j = states[n], states[n + 1]
+            p_transition[i, j] += 1
+    for i in range(len(p_transition)):
+        p_transition[i, :] = p_transition[i, :] / sum(p_transition[i, :])
+
+    # p_emission is the probability of an output given a state.
+    # C(s,o)|C(s) where o is an output and s is a state.
+    p_emission = np.zeros((N, M))
+    if pseudo_emission:
+        p_emission = p_emission + pseudo_emission
+    p_emission = np.ones((N, M))
+    for outputs, states in zip(training_outputs, training_states):
+        for o, s in zip(outputs, states):
+            p_emission[s, o] += 1
+    for i in range(len(p_emission)):
+        p_emission[i, :] = p_emission[i, :] / sum(p_emission[i, :])
+
+    return p_initial, p_transition, p_emission
+
+
+def _argmaxes(vector, allowance=None):
+    """Return indices of the maximum values aong the vector (PRIVATE)."""
+    return [np.argmax(vector)]
+
+
+def find_states(markov_model, output):
+    """Find states in the given Markov model output.
+
+    Returns a list of (states, score) tuples.
+    """
+    mm = markov_model
+    N = len(mm.states)
+
+    # _viterbi does calculations in log space.  Add a tiny bit to the
+    # matrices so that the logs will not break.
+    lp_initial = np.log(mm.p_initial + VERY_SMALL_NUMBER)
+    lp_transition = np.log(mm.p_transition + VERY_SMALL_NUMBER)
+    lp_emission = np.log(mm.p_emission + VERY_SMALL_NUMBER)
+    # Change output into a list of indexes into the alphabet.
+    indexes = itemindex(mm.alphabet)
+    output = [indexes[x] for x in output]
+
+    # Run the viterbi algorithm.
+    results = _viterbi(N, lp_initial, lp_transition, lp_emission, output)
+
+    for i in range(len(results)):
+        states, score = results[i]
+        results[i] = [mm.states[x] for x in states], np.exp(score)
+    return results
+
+
+def _viterbi(N, lp_initial, lp_transition, lp_emission, output):
+    """Implement Viterbi algorithm to find most likely states for a given input (PRIVATE)."""
+    T = len(output)
+    # Store the backtrace in a NxT matrix.
+    backtrace = []  # list of indexes of states in previous timestep.
+    for i in range(N):
+        backtrace.append([None] * T)
+
+    # Store the best scores.
+    scores = np.zeros((N, T))
+    scores[:, 0] = lp_initial + lp_emission[:, output[0]]
+    for t in range(1, T):
+        k = output[t]
+        for j in range(N):
+            # Find the most likely place it came from.
+            i_scores = scores[:, t - 1] + lp_transition[:, j] + lp_emission[j, k]
+            indexes = _argmaxes(i_scores)
+            scores[j, t] = i_scores[indexes[0]]
+            backtrace[j][t] = indexes
+
+    # Do the backtrace.  First, find a good place to start.  Then,
+    # we'll follow the backtrace matrix to find the list of states.
+    # In the event of ties, there may be multiple paths back through
+    # the matrix, which implies a recursive solution.  We'll simulate
+    # it by keeping our own stack.
+    in_process = []  # list of (t, states, score)
+    results = []  # return values.  list of (states, score)
+    indexes = _argmaxes(scores[:, T - 1])  # pick the first place
+    for i in indexes:
+        in_process.append((T - 1, [i], scores[i][T - 1]))
+    while in_process:
+        t, states, score = in_process.pop()
+        if t == 0:
+            results.append((states, score))
+        else:
+            indexes = backtrace[states[0]][t]
+            for i in indexes:
+                in_process.append((t - 1, [i] + states, score))
+    return results
+
+
+def _normalize(matrix):
+    """Normalize matrix object (PRIVATE)."""
+    if len(matrix.shape) == 1:
+        matrix = matrix / sum(matrix)
+    elif len(matrix.shape) == 2:
+        # Normalize by rows.
+        for i in range(len(matrix)):
+            matrix[i, :] = matrix[i, :] / sum(matrix[i, :])
+    else:
+        raise ValueError("I cannot handle matrixes of that shape")
+    return matrix
+
+
+def _uniform_norm(shape):
+    """Normalize a uniform matrix (PRIVATE)."""
+    matrix = np.ones(shape)
+    return _normalize(matrix)
+
+
+def _random_norm(shape):
+    """Normalize a random matrix (PRIVATE)."""
+    matrix = np.random.random(shape)
+    return _normalize(matrix)
+
+
+def _copy_and_check(matrix, desired_shape):
+    """Copy a matrix and check its dimension. Normalize at the end (PRIVATE)."""
+    # Copy the matrix.
+    matrix = np.array(matrix, copy=1)
+    # Check the dimensions.
+    if matrix.shape != desired_shape:
+        raise ValueError("Incorrect dimension")
+    # Make sure it's normalized.
+    if len(matrix.shape) == 1:
+        if np.fabs(sum(matrix) - 1.0) > 0.01:
+            raise ValueError("matrix not normalized to 1.0")
+    elif len(matrix.shape) == 2:
+        for i in range(len(matrix)):
+            if np.fabs(sum(matrix[i]) - 1.0) > 0.01:
+                raise ValueError("matrix %d not normalized to 1.0" % i)
+    else:
+        raise ValueError("I don't handle matrices > 2 dimensions")
+    return matrix
+
+
+def _logsum(matrix):
+    """Implement logsum for a matrix object (PRIVATE)."""
+    if len(matrix.shape) > 1:
+        vec = np.reshape(matrix, (np.prod(matrix.shape),))
+    else:
+        vec = matrix
+    sum = LOG0
+    for num in vec:
+        sum = logaddexp(sum, num)
+    return sum
+
+
+def _logvecadd(logvec1, logvec2):
+    """Implement a log sum for two vector objects (PRIVATE)."""
+    assert len(logvec1) == len(logvec2), "vectors aren't the same length"
+    sumvec = np.zeros(len(logvec1))
+    for i in range(len(logvec1)):
+        sumvec[i] = logaddexp(logvec1[i], logvec2[i])
+    return sumvec
+
+
+def _exp_logsum(numbers):
+    """Return the exponential of a logsum (PRIVATE)."""
+    sum = _logsum(numbers)
+    return np.exp(sum)