Issue #27181 remove geometric_mean and defer for 3.7.

1 files changed, 1 insertions, 268 deletions

diff --git a/Lib/statistics.py b/Lib/statistics.py
index 7d53e0c..30fe55c 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -11,7 +11,6 @@ Calculating averages
 Function            Description
 ==================  =============================================
 mean                Arithmetic mean (average) of data.
-geometric_mean      Geometric mean of data.
 harmonic_mean       Harmonic mean of data.
 median              Median (middle value) of data.
 median_low          Low median of data.
@@ -80,7 +79,7 @@ A single exception is defined: StatisticsError is a subclass of ValueError.
 __all__ = [ 'StatisticsError',
             'pstdev', 'pvariance', 'stdev', 'variance',
             'median',  'median_low', 'median_high', 'median_grouped',
-            'mean', 'mode', 'geometric_mean', 'harmonic_mean',
+            'mean', 'mode', 'harmonic_mean',
           ]
 
 import collections
@@ -287,229 +286,6 @@ def _fail_neg(values, errmsg='negative value'):
         yield x
 
 
-class _nroot_NS:
-    """Hands off! Don't touch!
-
-    Everything inside this namespace (class) is an even-more-private
-    implementation detail of the private _nth_root function.
-    """
-    # This class exists only to be used as a namespace, for convenience
-    # of being able to keep the related functions together, and to
-    # collapse the group in an editor. If this were C# or C++, I would
-    # use a Namespace, but the closest Python has is a class.
-    #
-    # FIXME possibly move this out into a separate module?
-    # That feels like overkill, and may encourage people to treat it as
-    # a public feature.
-    def __init__(self):
-        raise TypeError('namespace only, do not instantiate')
-
-    def nth_root(x, n):
-        """Return the positive nth root of numeric x.
-
-        This may be more accurate than ** or pow():
-
-        >>> math.pow(1000, 1.0/3)  #doctest:+SKIP
-        9.999999999999998
-
-        >>> _nth_root(1000, 3)
-        10.0
-        >>> _nth_root(11**5, 5)
-        11.0
-        >>> _nth_root(2, 12)
-        1.0594630943592953
-
-        """
-        if not isinstance(n, int):
-            raise TypeError('degree n must be an int')
-        if n < 2:
-            raise ValueError('degree n must be 2 or more')
-        if isinstance(x, decimal.Decimal):
-            return _nroot_NS.decimal_nroot(x, n)
-        elif isinstance(x, numbers.Real):
-            return _nroot_NS.float_nroot(x, n)
-        else:
-            raise TypeError('expected a number, got %s') % type(x).__name__
-
-    def float_nroot(x, n):
-        """Handle nth root of Reals, treated as a float."""
-        assert isinstance(n, int) and n > 1
-        if x < 0:
-            raise ValueError('domain error: root of negative number')
-        elif x == 0:
-            return math.copysign(0.0, x)
-        elif x > 0:
-            try:
-                isinfinity = math.isinf(x)
-            except OverflowError:
-                return _nroot_NS.bignum_nroot(x, n)
-            else:
-                if isinfinity:
-                    return float('inf')
-                else:
-                    return _nroot_NS.nroot(x, n)
-        else:
-            assert math.isnan(x)
-            return float('nan')
-
-    def nroot(x, n):
-        """Calculate x**(1/n), then improve the answer."""
-        # This uses math.pow() to calculate an initial guess for the root,
-        # then uses the iterated nroot algorithm to improve it.
-        #
-        # By my testing, about 8% of the time the iterated algorithm ends
-        # up converging to a result which is less accurate than the initial
-        # guess. [FIXME: is this still true?] In that case, we use the
-        # guess instead of the "improved" value. This way, we're never
-        # less accurate than math.pow().
-        r1 = math.pow(x, 1.0/n)
-        eps1 = abs(r1**n - x)
-        if eps1 == 0.0:
-            # r1 is the exact root, so we're done. By my testing, this
-            # occurs about 80% of the time for x < 1 and 30% of the
-            # time for x > 1.
-            return r1
-        else:
-            try:
-                r2 = _nroot_NS.iterated_nroot(x, n, r1)
-            except RuntimeError:
-                return r1
-            else:
-                eps2 = abs(r2**n - x)
-                if eps1 < eps2:
-                    return r1
-                return r2
-
-    def iterated_nroot(a, n, g):
-        """Return the nth root of a, starting with guess g.
-
-        This is a special case of Newton's Method.
-        https://en.wikipedia.org/wiki/Nth_root_algorithm
-        """
-        np = n - 1
-        def iterate(r):
-            try:
-                return (np*r + a/math.pow(r, np))/n
-            except OverflowError:
-                # If r is large enough, r**np may overflow. If that
-                # happens, r**-np will be small, but not necessarily zero.
-                return (np*r + a*math.pow(r, -np))/n
-        # With a good guess, such as g = a**(1/n), this will converge in
-        # only a few iterations. However a poor guess can take thousands
-        # of iterations to converge, if at all. We guard against poor
-        # guesses by setting an upper limit to the number of iterations.
-        r1 = g
-        r2 = iterate(g)
-        for i in range(1000):
-            if r1 == r2:
-                break
-            # Use Floyd's cycle-finding algorithm to avoid being trapped
-            # in a cycle.
-            # https://en.wikipedia.org/wiki/Cycle_detection#Tortoise_and_hare
-            r1 = iterate(r1)
-            r2 = iterate(iterate(r2))
-        else:
-            # If the guess is particularly bad, the above may fail to
-            # converge in any reasonable time.
-            raise RuntimeError('nth-root failed to converge')
-        return r2
-
-    def decimal_nroot(x, n):
-        """Handle nth root of Decimals."""
-        assert isinstance(x, decimal.Decimal)
-        assert isinstance(n, int)
-        if x.is_snan():
-            # Signalling NANs always raise.
-            raise decimal.InvalidOperation('nth-root of snan')
-        if x.is_qnan():
-            # Quiet NANs only raise if the context is set to raise,
-            # otherwise return a NAN.
-            ctx = decimal.getcontext()
-            if ctx.traps[decimal.InvalidOperation]:
-                raise decimal.InvalidOperation('nth-root of nan')
-            else:
-                # Preserve the input NAN.
-                return x
-        if x < 0:
-            raise ValueError('domain error: root of negative number')
-        if x.is_infinite():
-            return x
-        # FIXME this hasn't had the extensive testing of the float
-        # version _iterated_nroot so there's possibly some buggy
-        # corner cases buried in here. Can it overflow? Fail to
-        # converge or get trapped in a cycle? Converge to a less
-        # accurate root?
-        np = n - 1
-        def iterate(r):
-            return (np*r + x/r**np)/n
-        r0 = x**(decimal.Decimal(1)/n)
-        assert isinstance(r0, decimal.Decimal)
-        r1 = iterate(r0)
-        while True:
-            if r1 == r0:
-                return r1
-            r0, r1 = r1, iterate(r1)
-
-    def bignum_nroot(x, n):
-        """Return the nth root of a positive huge number."""
-        assert x > 0
-        # I state without proof that ⁿ√x ≈ ⁿ√2·ⁿ√(x//2)
-        # and that for sufficiently big x the error is acceptable.
-        # We now halve x until it is small enough to get the root.
-        m = 0
-        while True:
-            x //= 2
-            m += 1
-            try:
-                y = float(x)
-            except OverflowError:
-                continue
-            break
-        a = _nroot_NS.nroot(y, n)
-        # At this point, we want the nth-root of 2**m, or 2**(m/n).
-        # We can write that as 2**(q + r/n) = 2**q * ⁿ√2**r where q = m//n.
-        q, r = divmod(m, n)
-        b = 2**q * _nroot_NS.nroot(2**r, n)
-        return a * b
-
-
-# This is the (private) function for calculating nth roots:
-_nth_root = _nroot_NS.nth_root
-assert type(_nth_root) is type(lambda: None)
-
-
-def _product(values):
-    """Return product of values as (exponent, mantissa)."""
-    errmsg = 'mixed Decimal and float is not supported'
-    prod = 1
-    for x in values:
-        if isinstance(x, float):
-            break
-        prod *= x
-    else:
-        return (0, prod)
-    if isinstance(prod, Decimal):
-        raise TypeError(errmsg)
-    # Since floats can overflow easily, we calculate the product as a
-    # sort of poor-man's BigFloat. Given that:
-    #
-    #   x = 2**p * m  # p == power or exponent (scale), m = mantissa
-    #
-    # we can calculate the product of two (or more) x values as:
-    #
-    #   x1*x2 = 2**p1*m1 * 2**p2*m2 = 2**(p1+p2)*(m1*m2)
-    #
-    mant, scale = 1, 0  #math.frexp(prod)  # FIXME
-    for y in chain([x], values):
-        if isinstance(y, Decimal):
-            raise TypeError(errmsg)
-        m1, e1 = math.frexp(y)
-        m2, e2 = math.frexp(mant)
-        scale += (e1 + e2)
-        mant = m1*m2
-    return (scale, mant)
-
-
 # === Measures of central tendency (averages) ===
 
 def mean(data):
@@ -538,49 +314,6 @@ def mean(data):
     return _convert(total/n, T)
 
 
-def geometric_mean(data):
-    """Return the geometric mean of data.
-
-    The geometric mean is appropriate when averaging quantities which
-    are multiplied together rather than added, for example growth rates.
-    Suppose an investment grows by 10% in the first year, falls by 5% in
-    the second, then grows by 12% in the third, what is the average rate
-    of growth over the three years?
-
-    >>> geometric_mean([1.10, 0.95, 1.12])
-    1.0538483123382172
-
-    giving an average growth of 5.385%. Using the arithmetic mean will
-    give approximately 5.667%, which is too high.
-
-    ``StatisticsError`` will be raised if ``data`` is empty, or any
-    element is less than zero.
-    """
-    if iter(data) is data:
-        data = list(data)
-    errmsg = 'geometric mean does not support negative values'
-    n = len(data)
-    if n < 1:
-        raise StatisticsError('geometric_mean requires at least one data point')
-    elif n == 1:
-        x = data[0]
-        if isinstance(g, (numbers.Real, Decimal)):
-            if x < 0:
-                raise StatisticsError(errmsg)
-            return x
-        else:
-            raise TypeError('unsupported type')
-    else:
-        scale, prod = _product(_fail_neg(data, errmsg))
-        r = _nth_root(prod, n)
-        if scale:
-            p, q = divmod(scale, n)
-            s = 2**p * _nth_root(2**q, n)
-        else:
-            s = 1
-        return s*r
-
-
 def harmonic_mean(data):
     """Return the harmonic mean of data.