[fix] 1D and 2D graph generation

Author: Pierre Gerard

src/densite.py

@@ -5,13 +5,11 @@
 import densite_fonction
 
 
-
 class Densite1D:
  def __init__(self):
  self.iris = numpy.loadtxt('iris.txt')
  self.oneDimData = self._getOneDimDate()
 
-
  def _getOneDimDate(self):
  chosenClass = 1
  param = 0
@@ -21,40 +19,42 @@ def _getOneDimDate(self):
  oneDimData.append(l[param])
  return numpy.array(oneDimData)
 
-
  def getOneDimGraph(self):
- trainSetPlot, = plt.plot(self.oneDimData,numpy.array([i/1000. for i in range(len(self.oneDimData))]), 'bo')
+ trainSetPlot, = plt.plot(self.oneDimData, numpy.array([i / 1000. for i in range(len(self.oneDimData))]), 'bo')
 
  dg = densite_fonction.DensiteGaussienne(1)
  dg.train(self.oneDimData)
- x = numpy.linspace(3,7,1000)
- densParamPlot, = plt.plot(x,[dg.p(i) for i in x], 'red', label= "Densite parametrique")
-
+ x = numpy.linspace(3, 7, 1000)
+ densParamPlot, = plt.plot(x, [dg.p(i) for i in x], 'red', label="Densite parametrique")
 
  sigma = 0.05
- dg = densite_fonction.DensiteParzen(1,sigma)
+ dg = densite_fonction.DensiteParzen(1, sigma)
  dg.train(self.oneDimData)
- x = numpy.linspace(3,7,1000)
- parzenParamPlotGrand, = plt.plot(x,[dg.p(i) for i in x], 'yellow', label= "Parzen sigma petit")
+ x = numpy.linspace(3, 7, 1000)
+ parzenParamPlotGrand, = plt.plot(x, [dg.p(i) for i in x], 'yellow', label="Parzen sigma petit")
 
  sigma = 1
- dg = densite_fonction.DensiteParzen(1,sigma)
+ dg = densite_fonction.DensiteParzen(1, sigma)
  dg.train(self.oneDimData)
- x = numpy.linspace(3,7,1000)
- parzenParamPlotPetit, = plt.plot(x,[dg.p(i) for i in x], 'green', label= "Parzen sigma grand")
+ x = numpy.linspace(3, 7, 1000)
+ parzenParamPlotPetit, = plt.plot(x, [dg.p(i) for i in x], 'green', label="Parzen sigma grand")
 
  sigma = 0.349
- dg = densite_fonction.DensiteParzen(1,sigma)
+ dg = densite_fonction.DensiteParzen(1, sigma)
  dg.train(self.oneDimData)
- x = numpy.linspace(3,7,1000)
- parzenParamPlotAdequat, = plt.plot(x,[dg.p(i) for i in x], 'blue', label= "Parzen sigma adequat")
+ x = numpy.linspace(3, 7, 1000)
+ parzenParamPlotAdequat, = plt.plot(x, [dg.p(i) for i in x], 'blue', label="Parzen sigma adequat")
 
- plt.legend([trainSetPlot, densParamPlot, parzenParamPlotGrand,parzenParamPlotPetit,parzenParamPlotAdequat], ["Ensemble entrainement", "Densite parametrique","Parzen sigma petit","Parzen sigma grand","Parzen sigma adequat"])
+ plt.legend([trainSetPlot, densParamPlot, parzenParamPlotGrand, parzenParamPlotPetit, parzenParamPlotAdequat],
+ ["Ensemble entrainement", "Densite parametrique", "Parzen sigma petit", "Parzen sigma grand",
+ "Parzen sigma adequat"])
  plt.axis([3, 7, 0, 1.7])
- plt.title('1D Gaussian and Parzen probability density')
+ title = '1D Gaussian and Parzen probability density'
+ plt.title(title)
  plt.xlabel('x')
  plt.ylabel('Probability density')
- plt.show()
+ plt.savefig(title.replace(" ", "_") + ".png")
+ plt.close()
 
 
 class Densite2D:
@@ -78,71 +78,66 @@ def getParamDensityGraph(self):
  min_x2 = min([i[1] for i in self.twoDimData])
  max_x2 = max(([i[1] for i in self.twoDimData]))
  nDots = 100
- plt.plot(numpy.array([i[0] for i in self.twoDimData]),numpy.array([i[1] for i in self.twoDimData]), 'bo')
-
-
- xgrid = numpy.linspace(min_x1,max_x1,num=nDots)
- ygrid = numpy.linspace(min_x2,max_x2,num=nDots)
- #theGrid = numpy.array(utilitaires.combine(xgrid,ygrid))
+ plt.plot(numpy.array([i[0] for i in self.twoDimData]), numpy.array([i[1] for i in self.twoDimData]), 'bo')
 
+ xgrid = numpy.linspace(min_x1, max_x1, num=nDots)
+ ygrid = numpy.linspace(min_x2, max_x2, num=nDots)
 
  dg = densite_fonction.DensiteGaussienne(2)
  dg.train(self.twoDimData)
 
- resZ = [[ 0 for i in range(nDots)] for j in range(nDots)]
+ resZ = [[0 for i in range(nDots)] for j in range(nDots)]
  for ix, x in enumerate(xgrid):
  for iy, y in enumerate(ygrid):
- resZ[ix][iy] = float(dg.p([x,y]))
-
- plt.contour(xgrid,ygrid,numpy.array(resZ))
+ resZ[ix][iy] = float(dg.p([x, y]))
 
+ plt.contour(xgrid, ygrid, numpy.array(resZ))
 
  plt.axis([min_x1, max_x1, min_x2, max_x2])
- plt.title('2D Gaussian border')
+ title = '2D Gaussian border'
+ plt.title(title)
  plt.xlabel('x1')
  plt.ylabel('x2')
- plt.show()
+ plt.savefig(title.replace(" ", "_") + ".png")
+ plt.close()
 
- def getParzenSmallSigma(self):
+ def getParzenGraph(self, sigma):
  min_x1 = min([i[0] for i in self.twoDimData])
  max_x1 = max([i[0] for i in self.twoDimData])
  min_x2 = min([i[1] for i in self.twoDimData])
  max_x2 = max(([i[1] for i in self.twoDimData]))
  nDots = 100
- plt.plot(numpy.array([i[0] for i in self.twoDimData]),numpy.array([i[1] for i in self.twoDimData]), 'bo')
-
+ plt.plot(numpy.array([i[0] for i in self.twoDimData]), numpy.array([i[1] for i in self.twoDimData]), 'bo')
 
- xgrid = numpy.linspace(min_x1,max_x1,num=nDots)
- ygrid = numpy.linspace(min_x2,max_x2,num=nDots)
+ xgrid = numpy.linspace(min_x1, max_x1, num=nDots)
+ ygrid = numpy.linspace(min_x2, max_x2, num=nDots)
 
- sigma = 0.5
- dg = densite_fonction.DensiteParzen(2,sigma)
+ dg = densite_fonction.DensiteParzen(2, sigma)
  dg.train(self.twoDimData)
 
- resZ = [[ 0 for i in range(nDots)] for j in range(nDots)]
+ resZ = [[0 for i in range(nDots)] for j in range(nDots)]
  for ix, x in enumerate(xgrid):
  for iy, y in enumerate(ygrid):
- resZ[ix][iy] = float(dg.p([x,y]))
-
- plt.contour(xgrid,ygrid,numpy.array(resZ))
+ resZ[ix][iy] = float(dg.p([x, y]))
 
+ plt.contour(xgrid, ygrid, numpy.array(resZ))
 
  plt.axis([min_x1, max_x1, min_x2, max_x2])
- plt.title('2D Gaussian border')
+ title = '2D Parzen sigma=' + str(sigma)
+ plt.title(title)
  plt.xlabel('x1')
  plt.ylabel('x2')
- plt.show()
-
- def getParzenBigSigma(self):
- pass
+ plt.savefig(title.replace(" ", "_") + ".png")
+ plt.close()
+ # plt.show()
 
- def getParzenGoodSigma(self):
- pass
 
 if __name__ == '__main__':
- #d1d = Densite1D()
- #d1d.getOneDimGraph()
+ d1d = Densite1D()
+ d1d.getOneDimGraph()
 
  d2d = Densite2D()
- #d2d.getParamDensityGraph()
- d2d.getParzenSmallSigma()
+ d2d.getParamDensityGraph()
+ d2d.getParzenGraph(0.08)
+ d2d.getParzenGraph(0.40)
+ d2d.getParzenGraph(4)

src/densite_fonction.py

@@ -56,7 +56,7 @@ class DensiteParzen:
  def __init__(self, n_dims, sigma):
  self._n_dims = n_dims
  self._sigma = sigma
- self._distanceFunction = utilitaires.minkowski_mat
+ self._distanceFunction = utilitaires.minkowski
 
  def train(self, train_data):
  self._data = train_data

src/utilitaires.py

@@ -5,18 +5,21 @@
 
 
 
-def minkowski_mat(x, Y, p=2):
+def minkowski(x, y, p=2.):
  """
- Calcule la distance Minkowski entre un vecteur x et une matrice Y
+ Calcule la distance Minkowski entre un vecteur x et une vecteur Y
  """
  if type(x) == float or type(x) == int:
  x = numpy.array([x])
- if type(Y) == float or type(Y) == int:
- Y = numpy.array([Y])
- if type(Y) == numpy.float64:
- return (numpy.abs(x - Y))
+ if type(y) == float or type(y) == int:
+ y = numpy.array([y])
+ if type(y) == numpy.float64:
+ return (numpy.abs(x - y))
  else:
- return (numpy.sum((numpy.abs(x - Y)) ** p, axis=1)) ** (1.0 / p)
+ sum = 0
+ for i in range(len(x)):
+ sum += numpy.abs(x[i] - y[i]) ** p
+ return sum ** (1.0 / p)
 
 
 ##