Python LinearAlgebra.linear_least_squares方法代碼示例

# 需要導入模塊: import LinearAlgebra [as 別名]
# 或者: from LinearAlgebra import linear_least_squares [as 別名]
    def testLinearLeastSquares2(self):
        """
        From bug #503733. Failing with dlapack_lite
        """
        import LinearAlgebra
        d = [0.49910197] + [0.998203938] * 49
        d1 = [0.000898030454] * 50
        def tridiagonal(sz):
            G = Numeric.zeros((sz,sz), Numeric.Float64)
            for i in range(sz):
                G[i,i] = d[i]
            for i in range(0,sz-1):
                G[i+1,i] = G[i,i+1] = d1[i]
            return G

        yfull = Numeric.array(
            [4.37016668e+18, 4.09591905e+18, 3.82167167e+18, 4.12952660e+18,
             2.60084719e+18, 2.05944452e+18, 1.69850960e+18, 1.51450383e+18,
             1.39419275e+18, 1.25264986e+18, 1.18187857e+18, 1.16772440e+18,
             1.17126300e+18, 1.13941580e+18, 1.17834000e+18, 1.20664860e+18,
             1.18895580e+18, 1.18895580e+18, 1.21726440e+18, 1.24557296e+18,
             1.22434149e+18, 1.23495719e+18, 1.24203436e+18, 1.22434160e+18,
             1.23495720e+18, 1.21372580e+18, 1.22434160e+18, 1.21018740e+18,
             1.22080300e+18, 1.15357020e+18, 1.19957160e+18, 1.18187880e+18,
             1.19249440e+18, 1.18895579e+18, 1.28449704e+18, 1.27742021e+18,
             1.30218984e+18, 1.30926700e+18, 1.25972716e+18, 1.15003156e+18,
             1.17126296e+18, 1.15710876e+18, 1.10756882e+18, 1.20311006e+18,
             1.29511286e+18, 1.28449726e+18, 1.29157446e+18, 1.44373273e+18,])
        for i in range(20, 40):
            G = tridiagonal(i)
            y = yfull[:i]
            A = LinearAlgebra.linear_least_squares(G, y)[0]
            total = Numeric.add.reduce(y)
            residual = Numeric.absolute(y - Numeric.dot(G, A))
            assert_eq(0.0, Numeric.add.reduce(residual)/total)

# 需要導入模塊: import LinearAlgebra [as 別名]
# 或者: from LinearAlgebra import linear_least_squares [as 別名]
def plotit(xs,ys,title,legends):
    #
    # Do it 
    #
    num_points=0
    for x in xs:
        num_points=num_points+len(x)
    print num_points,'data points'
    #x=[1,2,3,4,5,6,7,8,9]
    #y=[2,4,6,8,10,12,14,16,18]
    mat_fix=[]
    vec_fix=[]
    for x in xs:
        for point in x:
            mat_fix.append([point,1.0])
    for y in ys:
        for point in y:
            vec_fix.append(point)
    import LinearAlgebra
    sols,rsq,rank,junk=LinearAlgebra.linear_least_squares(Numeric.array(mat_fix),
                                                          Numeric.array(vec_fix))
    slope=sols[0]
    intercept=sols[1]
    print rsq
    rsq=float(rsq[0])
    print 'Slope: %.2f, Intercept: %.2f, R^2: %.2f' %(slope,intercept,rsq)
    file=dislin_driver.graf_mult3(xs,ys,title,'Simple E','PBE_ene',legends)
    return

# 需要導入模塊: import LinearAlgebra [as 別名]
# 或者: from LinearAlgebra import linear_least_squares [as 別名]
 def testLinearLeastSquares(self):
     """
     From bug #503733.
     """
     # XXX not positive on this yet
     import LinearAlgebra
     from RandomArray import seed, random
     seed(7,19)
     (n, m) = (180, 35)
     yp = random((n,m))
     y  = random(n)
     x, residuals, rank, sv = LinearAlgebra.linear_least_squares(yp, y)
     # shouldn't segfault.
     assert rank == m

# 需要導入模塊: import LinearAlgebra [as 別名]
# 或者: from LinearAlgebra import linear_least_squares [as 別名]
def fitPolynomial(order, points, values):
    if len(points) != len(values):
	raise ValueError, 'Inconsistent arguments'
    if type(order) != type(()):
	order = (order,)
    order = tuple(map(lambda n: n+1, order))
    if not _isSequence(points[0]):
	points = map(lambda p: (p,), points)
    if len(order) != len(points[0]):
	raise ValueError, 'Inconsistent arguments'
    if Numeric.multiply.reduce(order) > len(points):
	raise ValueError, 'Not enough points'
    matrix = []
    for p in points:
	matrix.append(Numeric.ravel(_powers(p, order)))
    matrix = Numeric.array(matrix)
    values = Numeric.array(values)
    #inv = LinearAlgebra.generalized_inverse(matrix)
    #coeff = Numeric.dot(inv, values)
    coeff = LinearAlgebra.linear_least_squares(matrix, values)[0]
    coeff = Numeric.reshape(coeff, order)
    return Polynomial(coeff)

# 需要導入模塊: import LinearAlgebra [as 別名]
# 或者: from LinearAlgebra import linear_least_squares [as 別名]
def lsf(data, a, n):
	xmat=setxmat(data, a, n)
	yvec=setyvec(data, n)
	return LinearAlgebra.linear_least_squares(xmat, yvec)

# 需要導入模塊: import LinearAlgebra [as 別名]
# 或者: from LinearAlgebra import linear_least_squares [as 別名]
import sys,numeric_version 
import RandomArray 
import LinearAlgebra 

print sys.version 
print "Numeric version:",numeric_version.version 

RandomArray.seed(123,456) 
a = RandomArray.normal(0,1,(100,10)) 
f = RandomArray.normal(0,1,(10,30)) 
e = RandomArray.normal(0,0.1,(100,30)) 
print "Got to seed:",RandomArray.get_seed() 

b = dot(a,f)+e 

(x,res,rank,s)=LinearAlgebra.linear_least_squares(a,b) 

f_res = sum((b-dot(a,f))**2) 
x_res = sum((b-dot(a,x))**2) 

print "'Planted' residues, upper bound for optimal residues:" 
print f_res 
print "Claimed residues:" 
print res 
print "Actual residues:" 
print x_res 
print "Ratio between actual and claimed (shoudl be 1):" 
print x_res/res 
print "Ratio between actual and planted (should be <1):" 
print x_res/f_res 
print "Ratio between claimed and planted (shoudl be <1):"