rem stringlengths 1 322k | add stringlengths 0 2.05M | context stringlengths 4 228k | meta stringlengths 156 215 |
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def subplot(Numy,Numx,win=0,lm=0*inches,rm=0*inches,tm=0*inches,bm=0*inches,ph=11*inches,pw=8.5*inches,dpi=75,ls=0.75*inches,rs=0.75*inches,ts=0.75*inches,bs=0.75*inches): | def subplot(Numy,Numx,win=0,lm=0*inches,rm=0*inches,tm=0*inches,bm=0*inches,ph=11*inches,pw=8.5*inches,dpi=75,ls=0.75*inches,rs=0.75*inches,ts=0.75*inches,bs=0.75*inches,color='black',frame=0): if type(color) is types.StringType: color = _colornum[color] | def subplot(Numy,Numx,win=0,lm=0*inches,rm=0*inches,tm=0*inches,bm=0*inches,ph=11*inches,pw=8.5*inches,dpi=75,ls=0.75*inches,rs=0.75*inches,ts=0.75*inches,bs=0.75*inches): # Use gist.plsys to change coordinate systems systems=[] ind = -1 Yspace = (ph-bm-tm)/float(Numy) Xspace = (pw-rm-lm)/float(Numx) for nY in range(Nu... | d31b1aed025e9765ed229fd77d6341f36f1473cc /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d31b1aed025e9765ed229fd77d6341f36f1473cc/Mplot.py |
change_palette() | change_palette(palette) | def surf(x,y,z,win=None,shade=0,edges=1,edge_color="black",phi=-45,theta=30, zscale=1.0,palette=None,gnomon=0): """Plot a three-dimensional wire-frame (surface): z=f(x,y) """ if win is None: pl3d.window3() else: pl3d.window3(win) pl3d.set_draw3_(0) pl3d.orient3(phi=phi*pi/180,theta=theta*pi/180) pl3d.light3() change_pa... | d31b1aed025e9765ed229fd77d6341f36f1473cc /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d31b1aed025e9765ed229fd77d6341f36f1473cc/Mplot.py |
derphi_aj = derphi(a_j) | derphi_aj = derphi(a_j) | def zoom(a_lo, a_hi, phi_lo, phi_hi, derphi_lo, phi, derphi, phi0, derphi0, c1, c2): maxiter = 10 i = 0 delta1 = 0.2 # cubic interpolant check delta2 = 0.1 # quadratic interpolant check phi_rec = phi0 a_rec = 0 while 1: # interpolate to find a trial step length between a_lo and a_hi # Need to choose interpolation her... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
return a_star, val_star | return a_star, val_star, valprime_star | def zoom(a_lo, a_hi, phi_lo, phi_hi, derphi_lo, phi, derphi, phi0, derphi0, c1, c2): maxiter = 10 i = 0 delta1 = 0.2 # cubic interpolant check delta2 = 0.1 # quadratic interpolant check phi_rec = phi0 a_rec = 0 while 1: # interpolate to find a trial step length between a_lo and a_hi # Need to choose interpolation her... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
global fc, gc fc = 0 gc = 0 | global _ls_fc, _ls_gc, _ls_ingfk _ls_fc = 0 _ls_gc = 0 _ls_ingfk = None | def line_search(f, myfprime, xk, pk, gfk, old_fval, old_old_fval, args=(), c1=1e-4, c2=0.9, amax=50): """Find alpha that satisfies strong Wolfe conditions. Uses the line search algorithm to enforce strong Wolfe conditions Wright and Nocedal, 'Numerical Optimization', 1999, pg. 59-60 For the zoom phase it uses an algo... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
global fc fc += 1 | global _ls_fc _ls_fc += 1 | def phi(alpha): global fc fc += 1 return f(xk+alpha*pk,*args) | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
global fc fc += len(xk)+1 | global _ls_fc, _ls_ingfk _ls_fc += len(xk)+1 | def phiprime(alpha): global fc fc += len(xk)+1 eps = myfprime[1] fprime = myfprime[0] newargs = (f,eps) + args return Num.dot(fprime(xk+alpha*pk,*newargs),pk) | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
return Num.dot(fprime(xk+alpha*pk,*newargs),pk) | _ls_ingfk = fprime(xk+alpha*pk,*newargs) return Num.dot(_ls_ingfk,pk) | def phiprime(alpha): global fc fc += len(xk)+1 eps = myfprime[1] fprime = myfprime[0] newargs = (f,eps) + args return Num.dot(fprime(xk+alpha*pk,*newargs),pk) | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | global _ls_gc, _ls_ingfk _ls_gc += 1 _ls_ingfk = fprime(xk+alpha*pk,*args) return Num.dot(_ls_ingfk,pk) | def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
alpha_star, fval_star = zoom(alpha0, alpha1, phi_a0, phi_a1, derphi_a0, phi, phiprime, phi0, derphi0, c1, c2) | alpha_star, fval_star, fprime_star = \ zoom(alpha0, alpha1, phi_a0, phi_a1, derphi_a0, phi, phiprime, phi0, derphi0, c1, c2) | def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
alpha_star, fval_star = zoom(alpha1, alpha0, phi_a1, phi_a0, derphi_a1, phi, phiprime, phi0, derphi0, c1, c2) | alpha_star, fval_star, fprime_star = \ zoom(alpha1, alpha0, phi_a1, phi_a0, derphi_a1, phi, phiprime, phi0, derphi0, c1, c2) | def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
return alpha_star, fc, gc, fval_star, old_fval | if fprime_star is not None: fprime_star = _ls_ingfk return alpha_star, _ls_fc, _ls_gc, fval_star, old_fval, fprime_star | def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
old_fval = f(x0,*args) old_old_fval = old_fval + 5000 func_calls += 1 | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=1.49e-8, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py | |
old_fval = f(x0,*args) old_old_fval = old_fval + 5000 func_calls += 1 | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=1.49e-8, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py | |
alpha_k, fc, gc, old_fval, old_old_fval = \ | alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \ | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=1.49e-8, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
if app_fprime: gfkp1 = apply(approx_fprime,(xkp1,f,epsilon)+args) func_calls = func_calls + gc + len(x0) + 1 else: gfkp1 = apply(fprime,(xkp1,)+args) grad_calls = grad_calls + gc + 1 | if gfkp1 is None: if app_fprime: gfkp1 = apply(approx_fprime,(xkp1,f,epsilon)+args) func_calls = func_calls + len(x0) + 1 else: gfkp1 = apply(fprime,(xkp1,)+args) grad_calls = grad_calls + 1 | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=1.49e-8, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
xk = x0 | def fmin_cg(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=1.49e-8, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm of Polak... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py | |
old_fval = f(xk,*args) old_old_fval = old_fval + 5000 | def fmin_cg(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=1.49e-8, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm of Polak... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py | |
alpha_k, fc, gc, old_fval, old_old_fval = \ | alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \ | def fmin_cg(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=1.49e-8, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm of Polak... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
if app_fprime: gfkp1 = apply(approx_fprime,(xk,f,epsilon)+args) func_calls = func_calls + gc + len(x0) + 1 else: gfkp1 = apply(fprime,(xk,)+args) grad_calls = grad_calls + gc + 1 | if gfkp1 is None: if app_fprime: gfkp1 = apply(approx_fprime,(xk,f,epsilon)+args) func_calls = func_calls + len(x0) + 1 else: gfkp1 = apply(fprime,(xk,)+args) grad_calls = grad_calls + 1 | def fmin_cg(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=1.49e-8, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm of Polak... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
fval = apply(f,(xk,)+args) | fval = old_fval | def fmin_cg(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=1.49e-8, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm of Polak... | 18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/18d62e07c7b0b8dd2d3680a3b704bd36ff6ae89b/optimize.py |
xsupi = 0 | xsupi = zeros(len(x0), x0.typecode()) | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | c18af3409c18c2b6d29bcf8c8977181b040d36d2 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/c18af3409c18c2b6d29bcf8c8977181b040d36d2/optimize.py |
if (curv <= 0): | if curv == 0.0: break elif curv < 0: | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | c18af3409c18c2b6d29bcf8c8977181b040d36d2 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/c18af3409c18c2b6d29bcf8c8977181b040d36d2/optimize.py |
if abs(p-p0) < tol: | if abs(p-p1) < tol: | def newton(func, x0, fprime=None, args=(), tol=1.48e-8, maxiter=50): """Given a function of a single variable and a starting point, find a nearby zero using Newton-Raphson. fprime is the derivative of the function. If not given, the Secant method is used. """ if fprime is not None: p0 = x0 for iter in range(maxiter)... | bc1289785603ce752fdf4b390e8156d8b8555638 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bc1289785603ce752fdf4b390e8156d8b8555638/minpack.py |
assert_array_almost_equal(f(3,[3],[-4]),[-36]) | assert_array_almost_equal(f(3,[3],[-4]),[[-36]]) | def check_gemm(self): for p in 'sd': f = getattr(fblas,p+'gemm',None) if f is None: continue assert_array_almost_equal(f(3,[3],[-4]),[-36]) assert_array_almost_equal(f(3,[3],[-4],3,[5]),[-21]) assert_array_almost_equal(f(1,[[1,2],[1,2]],[[3],[4]]),[[11],[11]]) assert_array_almost_equal(f(1,[[1,2]],[[3,3],[4,4]]),[[11,1... | 7e71688ec03fc1369210cbb8183a43673560809c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/7e71688ec03fc1369210cbb8183a43673560809c/test_blas.py |
assert_array_almost_equal(f(3j,[3-4j],[-4]),[-48-36j]) | assert_array_almost_equal(f(3j,[3-4j],[-4]),[[-48-36j]]) | def check_gemm(self): for p in 'sd': f = getattr(fblas,p+'gemm',None) if f is None: continue assert_array_almost_equal(f(3,[3],[-4]),[-36]) assert_array_almost_equal(f(3,[3],[-4],3,[5]),[-21]) assert_array_almost_equal(f(1,[[1,2],[1,2]],[[3],[4]]),[[11],[11]]) assert_array_almost_equal(f(1,[[1,2]],[[3,3],[4,4]]),[[11,1... | 7e71688ec03fc1369210cbb8183a43673560809c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/7e71688ec03fc1369210cbb8183a43673560809c/test_blas.py |
assert_array_almost_equal(f(1,[[1,2]],[[3],[4]]),[11]) | assert_array_almost_equal(f(1,[[1,2]],[[3],[4]]),[[11]]) | def check_gemm2(self): for p in 'sdcz': f = getattr(fblas,p+'gemm',None) if f is None: continue assert_array_almost_equal(f(1,[[1,2]],[[3],[4]]),[11]) assert_array_almost_equal(f(1,[[1,2],[1,2]],[[3],[4]]),[[11],[11]]) | 7e71688ec03fc1369210cbb8183a43673560809c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/7e71688ec03fc1369210cbb8183a43673560809c/test_blas.py |
The entropy dual function equals the negative log likelihood. | The entropy dual function is proportional to the negative log likelihood. | def dual(self, params=None, ignorepenalty=False): """The entropy dual function is defined for conditional models as L(theta) = sum_w q(w) log Z(w; theta) - sum_{w,x} q(w,x) [theta . f(w,x)] | 2b410e32da15949ab05dc2ca96d760da0485cce5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/2b410e32da15949ab05dc2ca96d760da0485cce5/maxentropy.py |
var = (a*b*1.0)*(a+b+1.0)/(a+b)**2.0 | var = (a*b*1.0)/(a+b+1.0)/(a+b)**2.0 | def _stats(self, a, b): mn = a *1.0 / (a + b) var = (a*b*1.0)*(a+b+1.0)/(a+b)**2.0 g1 = 2.0*(b-a)*sqrt((1.0+a+b)/(a*b)) / (2+a+b) g2 = 6.0*(a**3 + a**2*(1-2*b) + b**2*(1+b) - 2*a*b*(2+b)) g2 /= a*b*(a+b+2)*(a+b+3) return mn, var, g1, g2 | 37623a23e1c2341836cbdef3c7a8f5cb45ad2b10 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/37623a23e1c2341836cbdef3c7a8f5cb45ad2b10/distributions.py |
return Date(freq, yaer=tempDate.year, quarter=monthToQuarter(tempDate.month)) | return Date(freq, year=tempDate.year, quarter=monthToQuarter(tempDate.month)) | def thisday(freq): freq = corelib.fmtFreq(freq) tempDate = mx.DateTime.now() # if it is Saturday or Sunday currently, freq==B, then we want to use Friday if freq == 'B' and tempDate.day_of_week >= 5: tempDate -= (tempDate.day_of_week - 4) if freq == 'B' or freq == 'D' or freq == 'S': return Date(freq, mxDate=tempDat... | 9297846990ea0107d5b2dd438e6c1d184fea05b8 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/9297846990ea0107d5b2dd438e6c1d184fea05b8/tsdate.py |
def sum(self, axis=None): # Override the base class sum method for efficiency in the cases # axis=0 and axis=None. m, n = self.shape data = self.data if axis in (0, None): indptr = self.indptr out = empty(n, dtype=self.dtype) # The first element in column j has index indptr[j], the last # indptr[j+1] for j in xrange(n)... | 9d0e035693e0e9fbbcd4c00deced4aea257fbed5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/9d0e035693e0e9fbbcd4c00deced4aea257fbed5/sparse.py | ||
def sum(self, axis=None): # Override the base class sum method for efficiency in the cases # axis=1 and axis=None. m, n = self.shape data = self.data if axis in (1, None): out = empty(m, dtype=self.dtype) # The first element in row i has index indptr[i], the last # indptr[i+1] indptr = self.indptr for i in xrange(m): o... | 9d0e035693e0e9fbbcd4c00deced4aea257fbed5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/9d0e035693e0e9fbbcd4c00deced4aea257fbed5/sparse.py | ||
def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | 9d0e035693e0e9fbbcd4c00deced4aea257fbed5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/9d0e035693e0e9fbbcd4c00deced4aea257fbed5/sparse.py | ||
x = asarray(x).squeeze() | x = asarray(x) | def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | 9d0e035693e0e9fbbcd4c00deced4aea257fbed5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/9d0e035693e0e9fbbcd4c00deced4aea257fbed5/sparse.py |
if len(x) != len(seq): raise ValueError, "number of elements in source" \ " must be same as number of elements in" \ " destimation or 1" | else: if x.ndim == 2: if x.shape != (1, len(seq)): raise ValueError, \ "source and destination have incompatible "\ "dimensions" else: x = x.squeeze() | def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | 9d0e035693e0e9fbbcd4c00deced4aea257fbed5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/9d0e035693e0e9fbbcd4c00deced4aea257fbed5/sparse.py |
def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | 9d0e035693e0e9fbbcd4c00deced4aea257fbed5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/9d0e035693e0e9fbbcd4c00deced4aea257fbed5/sparse.py | ||
def __mul__(self, other): # self * other if isscalar(other) or (isdense(other) and rank(other)==0): # Was: new = lil_matrix(self.shape, dtype=self.dtype) new = self.copy() if other == 0: # Multiply by zero: return the zero matrix return new # Multiply this scalar by every element. new.data = [[val * other for... | 9d0e035693e0e9fbbcd4c00deced4aea257fbed5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/9d0e035693e0e9fbbcd4c00deced4aea257fbed5/sparse.py | ||
fsim[0] = apply(func,(x0,)+args) | fsim[0] = func(x0) | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
f = apply(func,(y,)+args) | f = func(y) | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
funcalls = N+1 while (funcalls < maxfun and iterations < maxiter): | while (fcalls[0] < maxfun and iterations < maxiter): | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
fxr = apply(func,(xr,)+args) funcalls = funcalls + 1 | fxr = func(xr) | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
fxe = apply(func,(xe,)+args) funcalls = funcalls + 1 | fxe = func(xe) | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
fxc = apply(func,(xc,)+args) funcalls = funcalls + 1 | fxc = func(xc) | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
fxcc = apply(func,(xcc,)+args) funcalls = funcalls + 1 | fxcc = func(xcc) | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
fsim[j] = apply(func,(sim[j],)+args) funcalls = funcalls + N | fsim[j] = func(sim[j]) | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
if funcalls >= maxfun: | if fcalls[0] >= maxfun: | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % funcalls | print " Function evaluations: %d" % fcalls[0] | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
retlist = x, fval, iterations, funcalls, warnflag | retlist = x, fval, iterations, fcalls[0], warnflag | def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using the downhill simplex algorithm. Description: Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. Inputs: func -- the Python function ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
app_fprime = 0 if fprime is None: app_fprime = 1 | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | |
func_calls = 0 grad_calls = 0 | func_calls, f = wrap_function(f, args) if fprime is None: grad_calls, myfprime = wrap_function(approx_fprime, (f, epsilon)) else: grad_calls, myfprime = wrap_function(fprime, args) gfk = myfprime(x0) | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
old_fval = f(x0,*args) | old_fval = f(x0) | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
func_calls += 1 if app_fprime: gfk = apply(approx_fprime,(x0,f,epsilon)+args) myfprime = (approx_fprime,epsilon) func_calls = func_calls + len(x0) + 1 else: gfk = apply(fprime,(x0,)+args) myfprime = fprime grad_calls = grad_calls + 1 | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | |
old_fval,old_old_fval,args=args) | old_fval,old_old_fval) | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
func_calls = func_calls + fc grad_calls = grad_calls + gc | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | |
old_fval,old_old_fval,args=args) | old_fval,old_old_fval) | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
func_calls = func_calls + fc grad_calls = grad_calls + gc | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | |
if app_fprime: gfkp1 = apply(approx_fprime,(xkp1,f,epsilon)+args) func_calls = func_calls + len(x0) + 1 else: gfkp1 = apply(fprime,(xkp1,)+args) grad_calls = grad_calls + 1 | gfkp1 = myfprime(xkp1) | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % func_calls print " Gradient evaluations: %d" % grad_calls | print " Function evaluations: %d" % func_calls[0] print " Gradient evaluations: %d" % grad_calls[0] | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % func_calls print " Gradient evaluations: %d" % grad_calls | print " Function evaluations: %d" % func_calls[0] print " Gradient evaluations: %d" % grad_calls[0] | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % func_calls print " Gradient evaluations: %d" % grad_calls | print " Function evaluations: %d" % func_calls[0] print " Gradient evaluations: %d" % grad_calls[0] | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
retlist = xk, fval, gfk, Hk, func_calls, grad_calls, warnflag | retlist = xk, fval, gfk, Hk, func_calls[0], grad_calls[0], warnflag | def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
app_fprime = 0 if fprime is None: app_fprime = 1 | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | |
func_calls = 0 grad_calls = 0 | func_calls, f = wrap_function(f, args) if fprime is None: grad_calls, myfprime = wrap_function(approx_fprime, (f, epsilon)) else: grad_calls, myfprime = wrap_function(fprime, args) gfk = myfprime(x0) | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
old_fval = f(xk,*args) | old_fval = f(xk) | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
func_calls +=1 if app_fprime: gfk = apply(approx_fprime,(x0,f,epsilon)+args) myfprime = (approx_fprime,epsilon) func_calls = func_calls + len(x0) + 1 else: gfk = apply(fprime,(x0,)+args) myfprime = fprime grad_calls = grad_calls + 1 | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | |
old_old_fval,args=args,c2=0.4) | old_old_fval,c2=0.4) | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
func_calls += fc grad_calls += gc | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | |
old_fval,old_old_fval,args=args) func_calls += fc grad_calls += gc | old_fval,old_old_fval) | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
if app_fprime: gfkp1 = apply(approx_fprime,(xk,f,epsilon)+args) func_calls = func_calls + len(x0) + 1 else: gfkp1 = apply(fprime,(xk,)+args) grad_calls = grad_calls + 1 | gfkp1 = myfprime(xk) | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % func_calls print " Gradient evaluations: %d" % grad_calls | print " Function evaluations: %d" % func_calls[0] print " Gradient evaluations: %d" % grad_calls[0] | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % func_calls print " Gradient evaluations: %d" % grad_calls | print " Function evaluations: %d" % func_calls[0] print " Gradient evaluations: %d" % grad_calls[0] | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % func_calls print " Gradient evaluations: %d" % grad_calls | print " Function evaluations: %d" % func_calls[0] print " Gradient evaluations: %d" % grad_calls[0] | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
retlist = xk, fval, func_calls, grad_calls, warnflag | retlist = xk, fval, func_calls[0], grad_calls[0], warnflag | def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function with nonlinear conjugate gradient algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm ... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | ||
fcalls = 0 gcalls = 0 | fcalls, f = wrap_function(f, args) gcalls, fprime = wrap_function(fprime, args) | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
old_fval = f(x0,*args) fcalls += 1 | old_fval = f(x0) | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
b = -apply(fprime,(xk,)+args) gcalls = gcalls + 1 | b = -fprime(xk) | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
Ap = apply(approx_fhess_p,(xk,psupi,fprime,epsilon)+args) gcalls = gcalls + 2 | Ap = approx_fhess_p(xk,psupi,fprime,epsilon) | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
Ap = apply(fhess_p,(xk,psupi)+args) | Ap = fhess_p(xk,psupi, *args) | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
alphak, fc, gc, old_fval = line_search_BFGS(f,xk,pk,gfk,old_fval,args) fcalls = fcalls + fc gcalls = gcalls + gc | alphak, fc, gc, old_fval = line_search_BFGS(f,xk,pk,gfk,old_fval) | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % fcalls print " Gradient evaluations: %d" % gcalls | print " Function evaluations: %d" % fcalls[0] print " Gradient evaluations: %d" % gcalls[0] | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % fcalls print " Gradient evaluations: %d" % gcalls | print " Function evaluations: %d" % fcalls[0] print " Gradient evaluations: %d" % gcalls[0] | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
retlist = xk, fval, fcalls, gcalls, hcalls, warnflag | retlist = xk, fval, fcalls[0], gcalls[0], hcalls, warnflag | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is no... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
si = Numeric.sign(xm-xf) + ((xm-xf)==0) | si = Num.sign(xm-xf) + ((xm-xf)==0) | def fminbound(func, x1, x2, args=(), xtol=1e-5, maxfun=500, full_output=0, disp=1): """Bounded minimization for scalar functions. Description: Finds a local minimizer of the scalar function func in the interval x1 < xopt < x2 using Brent's method. (See brent for auto-bracketing). Inputs: func -- the function to be... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
si = Numeric.sign(rat) + (rat == 0) | si = Num.sign(rat) + (rat == 0) | def fminbound(func, x1, x2, args=(), xtol=1e-5, maxfun=500, full_output=0, disp=1): """Bounded minimization for scalar functions. Description: Finds a local minimizer of the scalar function func in the interval x1 < xopt < x2 using Brent's method. (See brent for auto-bracketing). Inputs: func -- the function to be... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
global _powell_funcalls def _myfunc(alpha, func, x0, direc, args=()): funcargs = (x0 + alpha * direc,)+args return func(*funcargs) def _linesearch_powell(func, p, xi, args=(), tol=1e-3): | def _linesearch_powell(func, p, xi, tol=1e-3): | def bracket(func, xa=0.0, xb=1.0, args=(), grow_limit=110.0): """Given a function and distinct initial points, search in the downhill direction (as defined by the initital points) and return new points xa, xb, xc that bracket the minimum of the function: f(xa) > f(xb) < f(xc) """ _gold = 1.618034 _verysmall_num = 1e-21... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
global _powell_funcalls extra_args = (func, p, xi, args) alpha_min, fret, iter, num = brent(_myfunc, args=extra_args, full_output=1, tol=tol) | def myfunc(alpha): return func(p + alpha * xi) alpha_min, fret, iter, num = brent(myfunc, full_output=1, tol=tol) | def _linesearch_powell(func, p, xi, args=(), tol=1e-3): # line-search algorithm using fminbound # find the minimium of the function # func(x0+ alpha*direc) global _powell_funcalls extra_args = (func, p, xi, args) alpha_min, fret, iter, num = brent(_myfunc, args=extra_args, full_output=1, tol=tol) xi = alpha_min*xi _p... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
_powell_funcalls += num | def _linesearch_powell(func, p, xi, args=(), tol=1e-3): # line-search algorithm using fminbound # find the minimium of the function # func(x0+ alpha*direc) global _powell_funcalls extra_args = (func, p, xi, args) alpha_min, fret, iter, num = brent(_myfunc, args=extra_args, full_output=1, tol=tol) xi = alpha_min*xi _p... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | |
global _powell_funcalls | fcalls, func = wrap_function(func, args) | def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using modified Powell's method. Description: Uses a modification of Powell's method to find the minimum of a function of N variables Inputs: func -- the Python function or met... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
fval = squeeze(apply(func, (x,)+args)) _powell_funcalls = 1 | fval = squeeze(func(x)) | def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using modified Powell's method. Description: Uses a modification of Powell's method to find the minimum of a function of N variables Inputs: func -- the Python function or met... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
fval, x, direc1 = _linesearch_powell(func, x, direc1, args=args, tol=xtol*100) | fval, x, direc1 = _linesearch_powell(func, x, direc1, tol=xtol*100) | def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using modified Powell's method. Description: Uses a modification of Powell's method to find the minimum of a function of N variables Inputs: func -- the Python function or met... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
if _powell_funcalls >= maxfun: break | if fcalls[0] >= maxfun: break | def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using modified Powell's method. Description: Uses a modification of Powell's method to find the minimum of a function of N variables Inputs: func -- the Python function or met... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
fx2 = squeeze(apply(func, (x2,)+args)) _powell_funcalls +=1 | fx2 = squeeze(func(x2)) | def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using modified Powell's method. Description: Uses a modification of Powell's method to find the minimum of a function of N variables Inputs: func -- the Python function or met... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
fval, x, direc1 = _linesearch_powell(func, x, direc1, args=args, tol=xtol*100) | fval, x, direc1 = _linesearch_powell(func, x, direc1, tol=xtol*100) | def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using modified Powell's method. Description: Uses a modification of Powell's method to find the minimum of a function of N variables Inputs: func -- the Python function or met... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
if _powell_funcalls >= maxfun: | if fcalls[0] >= maxfun: | def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using modified Powell's method. Description: Uses a modification of Powell's method to find the minimum of a function of N variables Inputs: func -- the Python function or met... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
print " Function evaluations: %d" % _powell_funcalls | print " Function evaluations: %d" % fcalls[0] | def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using modified Powell's method. Description: Uses a modification of Powell's method to find the minimum of a function of N variables Inputs: func -- the Python function or met... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
retlist = x, fval, direc, iter, _powell_funcalls, warnflag | retlist = x, fval, direc, iter, fcalls[0], warnflag | def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0): """Minimize a function using modified Powell's method. Description: Uses a modification of Powell's method to find the minimum of a function of N variables Inputs: func -- the Python function or met... | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
if __name__ == "__main__": import string | def main(): | def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py | ||
if __name__ == "__main__": main() | def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | 926e615eebcd9f2ca3373b1887f74b532b10bda4 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/926e615eebcd9f2ca3373b1887f74b532b10bda4/optimize.py |
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