bugged stringlengths 4 228k | fixed stringlengths 0 96.3M | __index_level_0__ int64 0 481k |
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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): # 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... | 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... | 400 |
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... | 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... | 401 |
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... | 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... | 402 |
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... | 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... | 403 |
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... | 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... | 404 |
def phi(alpha): global fc fc += 1 return f(xk+alpha*pk,*args) | def phi(alpha): global _ls_fc _ls_fc += 1 return f(xk+alpha*pk,*args) | 405 |
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) | def phiprime(alpha): global _ls_fc, _ls_ingfk _ls_fc += len(xk)+1 eps = myfprime[1] fprime = myfprime[0] newargs = (f,eps) + args return Num.dot(fprime(xk+alpha*pk,*newargs),pk) | 406 |
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) | 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) | 407 |
def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | 408 |
def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | 409 |
def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | 410 |
def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | def phiprime(alpha): global gc gc += 1 return Num.dot(fprime(xk+alpha*pk,*args),pk) | 411 |
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 (... | defold_fval = f(x0,*args) old_old_fval = old_fval + 5000 func_calls += 1 fmin_bfgs(f,old_fval = f(x0,*args) old_old_fval = old_fval + 5000 func_calls += 1 x0,old_fval = f(x0,*args) old_old_fval = old_fval + 5000 func_calls += 1 fprime=None,old_fval = f(x0,*args) old_old_fval = old_fval + 5000 func_calls += 1 args=(),ol... | 412 |
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 (... | 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 (... | 413 |
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 (... | 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 (... | 414 |
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 (... | 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 (... | 415 |
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... | 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... | 416 |
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... | 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... | 417 |
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... | 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... | 418 |
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... | 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... | 419 |
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... | 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... | 420 |
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... | 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... | 421 |
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... | 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... | 422 |
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)... | 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)... | 423 |
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... | 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... | 424 |
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... | 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... | 425 |
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]]) | 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]]) | 426 |
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)] | 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)] | 427 |
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 | 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 | 428 |
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... | 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... | 429 |
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)... | defsum(self,axis=None):#Overridethebaseclasssummethodforefficiencyinthecases#axis=0andaxis=None.m,n=self.shapedata=self.dataifaxisin(0,None):indptr=self.indptrout=empty(n,dtype=self.dtype)#Thefirstelementincolumnjhasindexindptr[j],thelast#indptr[j+1]forjinxrange(n):out[j]=data[indptr[j]:indptr[j+1]].sum()ifaxis==0:#Out... | 430 |
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... | defsum(self,axis=None):#Overridethebaseclasssummethodforefficiencyinthecases#axis=1andaxis=None.m,n=self.shapedata=self.dataifaxisin(1,None):out=empty(m,dtype=self.dtype)#Thefirstelementinrowihasindexindptr[i],thelast#indptr[i+1]indptr=self.indptrforiinxrange(m):out[i]=data[indptr[i]:indptr[i+1]].sum()ifaxis==1:#Output... | 431 |
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 ... | 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 ... | 432 |
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 ... | 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 ... | 433 |
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 ... | 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 ... | 434 |
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 ... | 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_matri... | 435 |
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... | def__mul__(self,other):#self*otherifisscalar(other)or(isdense(other)andrank(other)==0):#Was:new=lil_matrix(self.shape,dtype=self.dtype)new=self.copy()ifother==0:#Multiplybyzero:returnthezeromatrixreturnnew#Multiplythisscalarbyeveryelement.new.data=[[val*otherforvalinrowvals]forrowvalsinnew.data]returnnewelse:returnself... | 436 |
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 ... | 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 ... | 437 |
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 ... | 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 ... | 438 |
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 ... | 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 ... | 439 |
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 ... | 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 ... | 440 |
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 ... | 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 ... | 441 |
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 ... | 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 ... | 442 |
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 ... | 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 ... | 443 |
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 ... | 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 ... | 444 |
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 ... | 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 ... | 445 |
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 ... | 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 ... | 446 |
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 ... | 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 ... | 447 |
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 ... | 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 ... | 448 |
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 ... | 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 ... | 449 |
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 ... | 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 ... | 450 |
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 ... | 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 ... | 451 |
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 ... | 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 ... | 452 |
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 ... | 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 ... | 453 |
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 ... | 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 ... | 454 |
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 ... | 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 ... | 455 |
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 ... | 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 ... | 456 |
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 ... | 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 ... | 457 |
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 ... | 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 ... | 458 |
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 ... | 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 ... | 459 |
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 ... | 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 ... | 460 |
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 ... | 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 ... | 461 |
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 ... | 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 ... | 462 |
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 ... | 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 ... | 463 |
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 ... | 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 ... | 464 |
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 ... | 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 ... | 465 |
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 ... | 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 ... | 466 |
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 ... | 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 ... | 467 |
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 ... | 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 ... | 468 |
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 ... | 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 ... | 469 |
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 ... | 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 ... | 470 |
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 ... | 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 ... | 471 |
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 ... | 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 ... | 472 |
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... | deffmin_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:Minimizethefunction,f,whosegradientisgivenbyfprimeusingtheNewton-CGmethod.fhess_pmustcomputethehessiantimesanarbitraryvector.Ifitisnotgiven,finite-differencesonfprimeareusedto... | 473 |
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... | 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... | 474 |
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... | 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... | 475 |
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... | 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... | 476 |
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... | 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... | 477 |
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... | 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... | 478 |
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... | 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... | 479 |
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... | 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... | 480 |
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... | 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... | 481 |
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... | 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... | 482 |
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... | 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... | 483 |
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... | 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... | 484 |
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... | 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... | 485 |
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... | 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) def myfunc(alpha): return func(p + alpha * xi) alpha_min, fret, iter, num = brent(myfunc, full_output=1, tol=tol) xi = alpha_min*xi _powell_funcalls += num return... | 486 |
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... | 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 re... | 487 |
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... | 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... | 488 |
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... | 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... | 489 |
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... | 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... | 490 |
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... | 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... | 491 |
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... | 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... | 492 |
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... | 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... | 493 |
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... | 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... | 494 |
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... | 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... | 495 |
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... | 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... | 496 |
def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | 497 |
def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | def_scalarfunc(*params):params=squeeze(asarray(params))returnfunc(params,*args) | 498 |
def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | 499 |
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