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NIFTy
Commit a35d8a83 authored by Theo Steininger's avatar Theo Steininger
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Merge branch 'master' of gitlab.mpcdf.mpg.de:ift/NIFTy

parents a09c6ed0 f51e2814
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      demos/critical_filtering.py
      View file @ a35d8a83
      from nifty import *
      from nifty.library.wiener_filter import WienerFilterEnergy
      import numpy as np
      from nifty import (VL_BFGS, DiagonalOperator, FFTOperator, Field,
      LinearOperator, PowerSpace, RelaxedNewton, RGSpace,
      SteepestDescent, create_power_operator, exp, log, sqrt)
      from nifty.library.critical_filter import CriticalPowerEnergy
      import plotly.offline as pl
      import plotly.graph_objs as go
      from nifty.library.wiener_filter import WienerFilterEnergy
      import plotly.graph_objs as go
      import plotly.offline as pl
      from mpi4py import MPI
      comm = MPI.COMM_WORLD
      rank = comm.rank
      np.random.seed(42)
      def plot_parameters(m,t,p, p_d):
      def plot_parameters(m, t, p, p_d):
      x = log(t.domain[0].kindex)
      m = fft.adjoint_times(m)
      ......@@ -20,7 +24,8 @@ def plot_parameters(m,t,p, p_d):
      p = p.val.get_full_data().real
      p_d = p_d.val.get_full_data().real
      pl.plot([go.Heatmap(z=m)], filename='map.html')
      pl.plot([go.Scatter(x=x,y=t), go.Scatter(x=x ,y=p), go.Scatter(x=x, y=p_d)], filename="t.html")
      pl.plot([go.Scatter(x=x, y=t), go.Scatter(x=x, y=p),
      go.Scatter(x=x, y=p_d)], filename="t.html")
      class AdjointFFTResponse(LinearOperator):
      ......@@ -36,6 +41,7 @@ class AdjointFFTResponse(LinearOperator):
      def _adjoint_times(self, x, spaces=None):
      return self.FFT(self.R.adjoint_times(x))
      @property
      def domain(self):
      return self._domain
      ......@@ -48,41 +54,40 @@ class AdjointFFTResponse(LinearOperator):
      def unitary(self):
      return False
      if __name__ == "__main__":
      distribution_strategy = 'not'
      # Set up position space
      s_space = RGSpace([128,128])
      s_space = RGSpace([128, 128])
      # s_space = HPSpace(32)
      # Define harmonic transformation and associated harmonic space
      fft = FFTOperator(s_space)
      h_space = fft.target[0]
      # Setting up power space
      # Set up power space
      p_space = PowerSpace(h_space, logarithmic=True,
      distribution_strategy=distribution_strategy)
      # Choosing the prior correlation structure and defining correlation operator
      # Choose the prior correlation structure and defining correlation operator
      p_spec = (lambda k: (.5 / (k + 1) ** 3))
      S = create_power_operator(h_space, power_spectrum=p_spec,
      distribution_strategy=distribution_strategy)
      # Drawing a sample sh from the prior distribution in harmonic space
      # Draw a sample sh from the prior distribution in harmonic space
      sp = Field(p_space, val=p_spec,
      distribution_strategy=distribution_strategy)
      sh = sp.power_synthesize(real_signal=True)
      # Choosing the measurement instrument
      # Choose the measurement instrument
      # Instrument = SmoothingOperator(s_space, sigma=0.01)
      Instrument = DiagonalOperator(s_space, diagonal=1.)
      # Instrument._diagonal.val[200:400, 200:400] = 0
      #Instrument._diagonal.val[64:512-64, 64:512-64] = 0
      # Instrument._diagonal.val[64:512-64, 64:512-64] = 0
      #Adding a harmonic transformation to the instrument
      # Add a harmonic transformation to the instrument
      R = AdjointFFTResponse(fft, Instrument)
      noise = 1.
      ......@@ -92,7 +97,7 @@ if __name__ == "__main__":
      std=sqrt(noise),
      mean=0)
      # Creating the mock data
      # Create mock data
      d = R(sh) + n
      # The information source
      ......@@ -103,52 +108,49 @@ if __name__ == "__main__":
      if rank == 0:
      pl.plot([go.Heatmap(z=d_data)], filename='data.html')
      # minimization strategy
      def convergence_measure(a_energy, iteration): # returns current energy
      # Minimization strategy
      def convergence_measure(a_energy, iteration): # returns current energy
      x = a_energy.value
      print (x, iteration)
      minimizer1 = RelaxedNewton(convergence_tolerance=1e-2,
      convergence_level=2,
      iteration_limit=3,
      callback=convergence_measure)
      minimizer2 = VL_BFGS(convergence_tolerance=0,
      iteration_limit=7,
      callback=convergence_measure,
      max_history_length=3)
      # Setting starting position
      flat_power = Field(p_space,val=1e-8)
      print(x, iteration)
      minimizer1 = RelaxedNewton(convergence_tolerance=1e-4,
      convergence_level=1,
      iteration_limit=5,
      callback=convergence_measure)
      minimizer2 = VL_BFGS(convergence_tolerance=1e-4,
      convergence_level=1,
      iteration_limit=20,
      callback=convergence_measure,
      max_history_length=20)
      minimizer3 = SteepestDescent(convergence_tolerance=1e-4,
      iteration_limit=100,
      callback=convergence_measure)
      # Set starting position
      flat_power = Field(p_space, val=1e-8)
      m0 = flat_power.power_synthesize(real_signal=True)
      t0 = Field(p_space, val=log(1./(1+p_space.kindex)**2))
      for i in range(500):
      S0 = create_power_operator(h_space, power_spectrum=exp(t0),
      distribution_strategy=distribution_strategy)
      distribution_strategy=distribution_strategy)
      # Initializing the nonlinear Wiener Filter energy
      # Initialize non-linear Wiener Filter energy
      map_energy = WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S0)
      # Solving the Wiener Filter analytically
      # Solve the Wiener Filter analytically
      D0 = map_energy.curvature
      m0 = D0.inverse_times(j)
      # Initializing the power energy with updated parameters
      power_energy = CriticalPowerEnergy(position=t0, m=m0, D=D0, smoothness_prior=10., samples=3)
      (power_energy, convergence) = minimizer1(power_energy)
      # Setting new power spectrum
      t0.val = power_energy.position.val.real
      # Initialize power energy with updated parameters
      power_energy = CriticalPowerEnergy(position=t0, m=m0, D=D0,
      smoothness_prior=10., samples=3)
      # Plotting current estimate
      print i
      if i%50 == 0:
      plot_parameters(m0,t0,log(sp), data_power)
      (power_energy, convergence) = minimizer2(power_energy)
      # Set new power spectrum
      t0.val = power_energy.position.val.real
      # Plot current estimate
      print(i)
      if i % 5 == 0:
      plot_parameters(m0, t0, log(sp), data_power)
      nifty/energies/line_energy.py
      View file @ a35d8a83
      ......@@ -16,10 +16,8 @@
      # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
      # and financially supported by the Studienstiftung des deutschen Volkes.
      from .energy import Energy
      class LineEnergy(Energy):
      class LineEnergy(object):
      """ Evaluates an underlying Energy along a certain line direction.
      Given an Energy class and a line direction, its position is parametrized by
      ......@@ -27,34 +25,31 @@ class LineEnergy(Energy):
      Parameters
      ----------
      position : float
      The step length parameter along the given line direction.
      line_position : float
      Defines the full spatial position of this energy via
      self.energy.position = zero_point + line_position*line_direction
      energy : Energy
      The Energy object which will be evaluated along the given direction.
      line_direction : Field
      Direction used for line evaluation.
      zero_point : Field *optional*
      Fixing the zero point of the line restriction. Used to memorize this
      position in new initializations. By the default the current position
      of the supplied `energy` instance is used (default : None).
      Direction used for line evaluation. Does not have to be normalized.
      offset : float *optional*
      Indirectly defines the zero point of the line via the equation
      energy.position = zero_point + offset*line_direction
      (default : 0.).
      Attributes
      ----------
      position : float
      line_position : float
      The position along the given line direction relative to the zero point.
      value : float
      The value of the energy functional at given `position`.
      gradient : float
      The gradient of the underlying energy instance along the line direction
      projected on the line direction.
      curvature : callable
      A positive semi-definite operator or function describing the curvature
      of the potential at given `position`.
      The value of the energy functional at the given position
      directional_derivative : float
      The directional derivative at the given position
      line_direction : Field
      Direction along which the movement is restricted. Does not have to be
      normalized.
      energy : Energy
      The underlying Energy at the `position` along the line direction.
      The underlying Energy at the given position
      Raises
      ------
      ......@@ -72,45 +67,49 @@ class LineEnergy(Energy):
      """
      def __init__(self, position, energy, line_direction, zero_point=None):
      super(LineEnergy, self).__init__(position=position)
      self.line_direction = line_direction
      if zero_point is None:
      zero_point = energy.position
      self._zero_point = zero_point
      def __init__(self, line_position, energy, line_direction, offset=0.):
      self._line_position = float(line_position)
      self._line_direction = line_direction
      position_on_line = self._zero_point + self.position*line_direction
      self.energy = energy.at(position=position_on_line)
      pos = energy.position \
      + (self._line_position-float(offset))*self._line_direction
      self.energy = energy.at(position=pos)
      def at(self, position):
      def at(self, line_position):
      """ Returns LineEnergy at new position, memorizing the zero point.
      Parameters
      ----------
      position : float
      line_position : float
      Parameter for the new position on the line direction.
      Returns
      -------
      out : LineEnergy
      LineEnergy object at new position with same zero point as `self`.
      """
      return self.__class__(position,
      return self.__class__(line_position,
      self.energy,
      self.line_direction,
      zero_point=self._zero_point)
      offset=self.line_position)
      @property
      def value(self):
      return self.energy.value
      @property
      def gradient(self):
      return self.energy.gradient.vdot(self.line_direction)
      def line_position(self):
      return self._line_position
      @property
      def line_direction(self):
      return self._line_direction
      @property
      def curvature(self):
      return self.energy.curvature
      def directional_derivative(self):
      res = self.energy.gradient.vdot(self.line_direction)
      if abs(res.imag) / max(abs(res.real), 1.) > 1e-12:
      print "directional derivative has non-negligible " \
      "imaginary part:", res
      return res.real
      nifty/minimization/descent_minimizer.py
      View file @ a35d8a83
      ......@@ -137,7 +137,7 @@ class DescentMinimizer(Loggable, object):
      # compute the the gradient for the current location
      gradient = energy.gradient
      gradient_norm = gradient.vdot(gradient)
      gradient_norm = gradient.norm()
      # check if position is at a flat point
      if gradient_norm == 0:
      ......@@ -147,7 +147,6 @@ class DescentMinimizer(Loggable, object):
      # current position is encoded in energy object
      descent_direction = self.get_descent_direction(energy)
      # compute the step length, which minimizes energy.value along the
      # search direction
      step_length, f_k, new_energy = \
      ......@@ -155,8 +154,12 @@ class DescentMinimizer(Loggable, object):
      energy=energy,
      pk=descent_direction,
      f_k_minus_1=f_k_minus_1)
      if f_k_minus_1 is None:
      delta=1e30
      else:
      delta = abs(f_k -f_k_minus_1)/max(abs(f_k),abs(f_k_minus_1),1.)
      f_k_minus_1 = energy.value
      tx1=energy.position-new_energy.position
      # check if new energy value is bigger than old energy value
      if (new_energy.value - energy.value) > 0:
      self.logger.info("Line search algorithm returned a new energy "
      ......@@ -165,7 +168,6 @@ class DescentMinimizer(Loggable, object):
      energy = new_energy
      # check convergence
      delta = abs(gradient).max() * (step_length/np.sqrt(gradient_norm))
      self.logger.debug("Iteration:%08u step_length=%3.1E "
      "delta=%3.1E energy=%3.1E" %
      (iteration_number, step_length, delta,
      ......
      nifty/minimization/line_searching/line_search.py
      View file @ a35d8a83
      ......@@ -63,7 +63,7 @@ class LineSearch(Loggable, object):
      iteration of the line search procedure. (Default: None)
      """
      self.line_energy = LineEnergy(position=0.,
      self.line_energy = LineEnergy(line_position=0.,
      energy=energy,
      line_direction=pk)
      if f_k_minus_1 is not None:
      ......
      nifty/minimization/line_searching/line_search_strong_wolfe.py
      View file @ a35d8a83
      ......@@ -62,8 +62,8 @@ class LineSearchStrongWolfe(LineSearch):
      """
      def __init__(self, c1=1e-4, c2=0.9,
      max_step_size=50, max_iterations=10,
      max_zoom_iterations=10):
      max_step_size=1000000000, max_iterations=100,
      max_zoom_iterations=30):
      super(LineSearchStrongWolfe, self).__init__()
      ......@@ -103,20 +103,15 @@ class LineSearchStrongWolfe(LineSearch):
      """
      self._set_line_energy(energy, pk, f_k_minus_1=f_k_minus_1)
      c1 = self.c1
      c2 = self.c2
      max_step_size = self.max_step_size
      max_iterations = self.max_iterations
      # initialize the zero phis
      old_phi_0 = self.f_k_minus_1
      energy_0 = self.line_energy.at(0)
      phi_0 = energy_0.value
      phiprime_0 = energy_0.gradient
      if phiprime_0 == 0:
      self.logger.warn("Flat gradient in search direction.")
      return 0., 0.
      le_0 = self.line_energy.at(0)
      phi_0 = le_0.value
      phiprime_0 = le_0.directional_derivative
      assert phiprime_0<0, "input direction must be a descent direction"
      # set alphas
      alpha0 = 0.
      ......@@ -135,64 +130,67 @@ class LineSearchStrongWolfe(LineSearch):
      # start the minimization loop
      for i in xrange(max_iterations):
      energy_alpha1 = self.line_energy.at(alpha1)
      phi_alpha1 = energy_alpha1.value
      le_alpha1 = self.line_energy.at(alpha1)
      phi_alpha1 = le_alpha1.value
      if alpha1 == 0:
      self.logger.warn("Increment size became 0.")
      alpha_star = 0.
      phi_star = phi_0
      energy_star = energy_0
      le_star = le_0
      break
      if (phi_alpha1 > phi_0 + c1*alpha1*phiprime_0) or \
      ((phi_alpha1 >= phi_alpha0) and (i > 1)):
      (alpha_star, phi_star, energy_star) = self._zoom(
      if (phi_alpha1 > phi_0 + self.c1*alpha1*phiprime_0) or \
      ((phi_alpha1 >= phi_alpha0) and (i > 0)):
      (alpha_star, phi_star, le_star) = self._zoom(
      alpha0, alpha1,
      phi_0, phiprime_0,
      phi_alpha0,
      phiprime_alpha0,
      phi_alpha1,
      c1, c2)
      phi_alpha1)
      break
      phiprime_alpha1 = energy_alpha1.gradient
      if abs(phiprime_alpha1) <= -c2*phiprime_0:
      phiprime_alpha1 = le_alpha1.directional_derivative
      if abs(phiprime_alpha1) <= -self.c2*phiprime_0:
      alpha_star = alpha1
      phi_star = phi_alpha1
      energy_star = energy_alpha1
      le_star = le_alpha1
      break
      if phiprime_alpha1 >= 0:
      (alpha_star, phi_star, energy_star) = self._zoom(
      (alpha_star, phi_star, le_star) = self._zoom(
      alpha1, alpha0,
      phi_0, phiprime_0,
      phi_alpha1,
      phiprime_alpha1,
      phi_alpha0,
      c1, c2)
      phi_alpha0)
      break
      # update alphas
      alpha0, alpha1 = alpha1, min(2*alpha1, max_step_size)
      if alpha1 == max_step_size:
      print "reached max step size, bailing out"
      alpha_star = alpha1
      phi_star = phi_alpha1
      le_star = le_alpha1
      break
      phi_alpha0 = phi_alpha1
      phiprime_alpha0 = phiprime_alpha1
      # phi_alpha1 = self._phi(alpha1)
      else:
      # max_iterations was reached
      alpha_star = alpha1
      phi_star = phi_alpha1
      energy_star = energy_alpha1
      le_star = le_alpha1
      self.logger.error("The line search algorithm did not converge.")
      # extract the full energy from the line_energy
      energy_star = energy_star.energy
      energy_star = le_star.energy
      direction_length = pk.norm()
      step_length = alpha_star * direction_length
      return step_length, phi_star, energy_star
      def _zoom(self, alpha_lo, alpha_hi, phi_0, phiprime_0,
      phi_lo, phiprime_lo, phi_hi, c1, c2):
      phi_lo, phiprime_lo, phi_hi):
      """Performs the second stage of the line search algorithm.
      If the first stage was not successful then the Zoom algorithm tries to
      ......@@ -202,9 +200,10 @@ class LineSearchStrongWolfe(LineSearch):
      Parameters
      ----------
      alpha_lo : float
      The lower boundary for the step length interval.
      alph_hi : float
      The upper boundary for the step length interval.
      A boundary for the step length interval.
      Fulfills Wolfe condition 1.
      alpha_hi : float
      The other boundary for the step length interval.
      phi_0 : float
      Value of the energy at the starting point of the line search
      algorithm.
      ......@@ -219,10 +218,6 @@ class LineSearchStrongWolfe(LineSearch):
      phi_hi : float
      Value of the energy if we perform a step of length alpha_hi in
      descent direction.
      c1 : float
      Parameter for Armijo condition rule.
      c2 : float
      Parameter for curvature condition rule.
      Returns
      -------
      ......@@ -238,12 +233,13 @@ class LineSearchStrongWolfe(LineSearch):
      # define the cubic and quadratic interpolant checks
      cubic_delta = 0.2 # cubic
      quad_delta = 0.1 # quadratic
      phiprime_alphaj = 0.
      # initialize the most recent versions (j-1) of phi and alpha
      alpha_recent = 0
      phi_recent = phi_0
      assert phi_lo <= phi_0 + self.c1*alpha_lo*phiprime_0
      assert phiprime_lo*(alpha_hi-alpha_lo)<0.
      for i in xrange(max_iterations):
      #assert phi_lo <= phi_0 + self.c1*alpha_lo*phiprime_0
      #assert phiprime_lo*(alpha_hi-alpha_lo)<0.
      delta_alpha = alpha_hi - alpha_lo
      if delta_alpha < 0:
      a, b = alpha_hi, alpha_lo
      ......@@ -262,28 +258,28 @@ class LineSearchStrongWolfe(LineSearch):
      quad_check = quad_delta * delta_alpha
      alpha_j = self._quadmin(alpha_lo, phi_lo, phiprime_lo,
      alpha_hi, phi_hi)
      # If quadratic was not successfull, try bisection
      # If quadratic was not successful, try bisection
      if (alpha_j is None) or (alpha_j > b - quad_check) or \
      (alpha_j < a + quad_check):
      alpha_j = alpha_lo + 0.5*delta_alpha
      # Check if the current value of alpha_j is already sufficient
      energy_alphaj = self.line_energy.at(alpha_j)
      phi_alphaj = energy_alphaj.value
      le_alphaj = self.line_energy.at(alpha_j)
      phi_alphaj = le_alphaj.value
      # If the first Wolfe condition is not met replace alpha_hi
      # by alpha_j
      if (phi_alphaj > phi_0 + c1*alpha_j*phiprime_0) or\
      if (phi_alphaj > phi_0 + self.c1*alpha_j*phiprime_0) or\
      (phi_alphaj >= phi_lo):
      alpha_recent, phi_recent = alpha_hi, phi_hi
      alpha_hi, phi_hi = alpha_j, phi_alphaj
      else:
      phiprime_alphaj = energy_alphaj.gradient
      phiprime_alphaj = le_alphaj.directional_derivative
      # If the second Wolfe condition is met, return the result
      if abs(phiprime_alphaj) <= -c2*phiprime_0:
      if abs(phiprime_alphaj) <= -self.c2*phiprime_0:
      alpha_star = alpha_j
      phi_star = phi_alphaj
      energy_star = energy_alphaj
      le_star = le_alphaj
      break
      # If not, check the sign of the slope
      if phiprime_alphaj*delta_alpha >= 0:
      ......@@ -296,12 +292,12 @@ class LineSearchStrongWolfe(LineSearch):
      phiprime_alphaj)
      else:
      alpha_star, phi_star, energy_star = \
      alpha_j, phi_alphaj, energy_alphaj
      alpha_star, phi_star, le_star = \
      alpha_j, phi_alphaj, le_alphaj
      self.logger.error("The line search algorithm (zoom) did not "
      "converge.")
      return alpha_star, phi_star, energy_star
      return alpha_star, phi_star, le_star
      def _cubicmin(self, a, fa, fpa, b, fb, c, fc):
      """Estimating the minimum with cubic interpolation.
      ......
      test/test_minimization/test_descent_minimizers.py
      View file @ a35d8a83
      ......@@ -43,7 +43,8 @@ class Test_DescentMinimizers(unittest.TestCase):
      covariance = DiagonalOperator(space, diagonal=covariance_diagonal)
      energy = QuadraticPotential(position=starting_point,
      eigenvalues=covariance)
      minimizer = minimizer_class(iteration_limit=30)
      minimizer = minimizer_class(iteration_limit=30,
      convergence_tolerance=1e-10)
      (energy, convergence) = minimizer(energy)
      ......
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