mjo/interpolation.py

   1 from sage.all import *
   2 from misc import product
   3
   4 def lagrange_coefficient(k, x, xs):
   5     """
   6     Returns the coefficient function l_{k}(variable) of y_{k} in the
   7     Lagrange polynomial of f. See,
   8
   9       http://en.wikipedia.org/wiki/Lagrange_polynomial
  10
  11     for more information.
  12
  13     INPUT:
  14
  15       - ``k`` -- the index of the coefficient.
  16
  17       - ``x`` -- the symbolic variable to use for the first argument
  18         of l_{k}.
  19
  20       - ``xs`` -- The list of points at which the function values are
  21         known.
  22
  23     OUTPUT:
  24
  25     A symbolic function of one variable.
  26
  27     TESTS::
  28
  29         sage: xs = [ -pi/2, -pi/6, 0, pi/6, pi/2 ]
  30         sage: lagrange_coefficient(0, x, xs)
  31         1/8*(pi - 6*x)*(pi - 2*x)*(pi + 6*x)*x/pi^4
  32
  33     """
  34     numerator = product([x - xs[j] for j in range(0, len(xs)) if j != k])
  35     denominator = product([xs[k] - xs[j] for j in range(0, len(xs)) if j != k])
  36
  37     return (numerator / denominator)
  38
  39
  40
  41 def lagrange_polynomial(x, xs, ys):
  42     """
  43     Return the Lagrange form of the interpolation polynomial in `x` of
  44     at the points (xs[k], ys[k]).
  45
  46     INPUT:
  47
  48       - ``x`` - The independent variable of the resulting polynomial.
  49
  50       - ``xs`` - The list of points at which we interpolate `f`.
  51
  52       - ``ys`` - The function values at `xs`.
  53
  54     OUTPUT:
  55
  56     A symbolic function (polynomial) interpolating each (xs[k], ys[k]).
  57
  58     TESTS::
  59
  60         sage: xs = [ -pi/2, -pi/6, 0, pi/6, pi/2 ]
  61         sage: ys = map(sin, xs)
  62         sage: L = lagrange_polynomial(x, xs, ys)
  63         sage: expected  = 27/16*(pi - 6*x)*(pi - 2*x)*(pi + 2*x)*x/pi^4
  64         sage: expected -= 1/8*(pi - 6*x)*(pi - 2*x)*(pi + 6*x)*x/pi^4
  65         sage: expected -= 1/8*(pi - 6*x)*(pi + 2*x)*(pi + 6*x)*x/pi^4
  66         sage: expected += 27/16*(pi - 2*x)*(pi + 2*x)*(pi + 6*x)*x/pi^4
  67         sage: bool(L == expected)
  68         True
  69
  70     """
  71     ls = [ lagrange_coefficient(k, x, xs) for k in range(0, len(xs)) ]
  72     sigma = sum([ ys[k] * ls[k] for k in range(0, len(xs)) ])
  73     return sigma
  74
  75
  76
  77 def divided_difference_coefficients(xs):
  78     """
  79     Assuming some function `f`, compute the coefficients of the
  80     divided difference f[xs[0], ..., xs[n]].
  81
  82     TESTS:
  83
  84         sage: divided_difference_coefficients([0])
  85         [1]
  86         sage: divided_difference_coefficients([0, pi])
  87         [-1/pi, 1/pi]
  88         sage: divided_difference_coefficients([0, pi, 2*pi])
  89         [1/2/pi^2, -1/pi^2, 1/2/pi^2]
  90
  91     """
  92     coeffs = [ product([ (QQ(1) / (xj - xi)) for xi in xs if xi != xj ])
  93                for xj in xs ]
  94     return coeffs
  95
  96 def divided_difference(f, xs):
  97     """
  98     Return the Newton divided difference of `f` at the points
  99     `xs`. Reference:
 100
 101       http://en.wikipedia.org/wiki/Divided_differences
 102
 103     INPUT:
 104
 105       - ``f`` -- The function whose divided difference we seek.
 106
 107       - ``xs`` -- The list of points at which to compute `f`.
 108
 109     OUTPUT:
 110
 111     The divided difference of `f` at ``xs``.
 112
 113     TESTS::
 114
 115         sage: divided_difference(sin, [0])
 116         0
 117         sage: divided_difference(sin, [0, pi])
 118         0
 119         sage: divided_difference(sin, [0, pi, 2*pi])
 120         0
 121
 122     We try something entirely symbolic::
 123
 124         sage: f = function('f', x)
 125         sage: divided_difference(f, [x])
 126         f(x)
 127         sage: x1,x2 = var('x1,x2')
 128         sage: divided_difference(f, [x1,x2])
 129         (f(x1) - f(x2))/(x1 - x2)
 130
 131     """
 132     if (len(xs) == 1):
 133         # Avoid that goddamned DeprecationWarning when we have a named
 134         # argument but don't know what it is.
 135         if len(f.variables()) == 0:
 136             return f(xs[0])
 137         else:
 138             v = f.variables()[0]
 139             return f({ v: xs[0] })
 140
 141     # Use the recursive definition.
 142     numerator  = divided_difference(f, xs[1:])
 143     numerator -= divided_difference(f, xs[:-1])
 144     return numerator / (xs[-1] - xs[0])
 145
 146
 147 def newton_polynomial(f, x, xs):
 148     """
 149     Return the Newton form of the interpolating polynomial of `f` at
 150     the points `xs` in the variable `x`.
 151
 152     INPUT:
 153
 154       - ``f`` -- The function to interpolate.
 155
 156       - ``x`` -- The independent variable to use for the interpolating
 157         polynomial.
 158
 159       - ``xs`` -- The list of points at which to interpolate `f`.
 160
 161     OUTPUT:
 162
 163     A symbolic function.
 164
 165     TESTS:
 166
 167         sage: xs = [ -pi/2, -pi/6, 0, pi/6, pi/2 ]
 168         sage: ys = map(sin, xs)
 169         sage: L = lagrange_polynomial(x, xs, ys)
 170         sage: N = newton_polynomial(sin, x, xs)
 171         sage: bool(N == L)
 172         True
 173
 174     """
 175     degree = len(xs) - 1
 176
 177     N = SR(0)
 178
 179     for k in range(0, degree+1):
 180         term  = divided_difference(f, xs[:k+1])
 181         term *= product([ x - xk for xk in xs[:k]])
 182         N += term
 183
 184     return N