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validation minimise added, cv incomplete, names in FDataGrid modified
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@mcarbajo
mcarbajo committed Nov 21, 2017
1 parent 77d76bd commit e0985de
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197 changes: 118 additions & 79 deletions fda/FDataGrid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,79 +20,82 @@ class FDataGrid:
data_matrix (numpy.ndarray): a matrix where each row contains the
values of a functional datum evaluated at the
points of discretisation.
argvals (numpy.ndarray): an array containing the points of
sample_points (numpy.ndarray): an array containing the points of
discretisation where values have been recorded or a list of lists
with each of the list containing the points of dicretisation for
each axis.
argvals_range (tuple or list): contains the edges of the interval in
which the functional data is considered to exist.
sample_range (tuple or list): contains the edges of the interval
in which the functional data is considered to exist.
names (list): list containing the names of the data set, x label, y
label, z label and so on.
Examples:
The number of columns of data_matrix have to be the length of argvals.
The number of columns of data_matrix have to be the length of
sample_points.
>>> FDataGrid(numpy.array([1,2,4,5,8]), range(6))
Traceback (most recent call last):
....
ValueError: Incorrect dimension in data_matrix and argvals arguments.
ValueError: Incorrect dimension in data_matrix and sample_points.
"""
def __init__(self, data_matrix, argvals=None, argvals_range=None,
names=None):
def __init__(self, data_matrix, sample_points=None,
sample_range=None, names=None):
"""
Args:
data_matrix (array_like): a matrix where each row contains the
values of a functional datum evaluated at the
points of discretisation.
argvals (array_like, optional): an array containing the points of
discretisation where values have been recorded or a list of
lists with each of the list containing the points of
sample_points (array_like, optional): an array containing the
points of discretisation where values have been recorded or a list
of lists with each of the list containing the points of
dicretisation for each axis.
argvals_range (tuple or list, optional): contains the edges of
the interval in which the functional data is considered to
sample_range (tuple or list, optional): contains the edges
of the interval in which the functional data is considered to
exist.
names (list): list containing the names of the data set, x label, y
label, z label and so on.
"""
self.data_matrix = numpy.asarray(data_matrix)
if self.data_matrix.ndim == 1:
self.data_matrix = numpy.array([self.data_matrix])
self.data_matrix = numpy.atleast_2d(data_matrix)
# TODO check dimensionality

if argvals is None:
if sample_points is None:
if self.data_matrix.ndim > 2:
self.argvals = [numpy.linspace(0, 1, self.data_matrix.shape[i])
for i in range(1, self.data_matrix.ndim)]
self.sample_points = [numpy.linspace(0, 1,
self.data_matrix.shape[i]) for i
in range(1, self.data_matrix.ndim)]
else:
self.argvals = numpy.linspace(0, 1, self.data_matrix.shape[1])
self.sample_points = numpy.linspace(0, 1,
self.data_matrix.shape[1])

else:
# Check that the dimension of the data matches the argvals list
self.argvals = numpy.asarray(argvals)
if (self.data_matrix.ndim == 1
or (self.data_matrix.ndim == 2
and len(self.argvals) != self.data_matrix.shape[1])
or (self.data_matrix.ndim > 2
and self.data_matrix.ndim != len(self.argvals) + 1)):
# Check that the dimension of the data matches the sample_points
# list
self.sample_points = numpy.asarray(sample_points)
if ((self.data_matrix.ndim == 2
and len(self.sample_points) != self.data_matrix.shape[1])
or (self.data_matrix.ndim > 2
and self.data_matrix.ndim != len(self.sample_points) + 1)):
raise ValueError("Incorrect dimension in data_matrix and "
"argvals arguments.")
"sample_points.")

if argvals_range is None:
if sample_range is None:
if self.data_matrix.ndim == 2:
self.argvals_range = (self.argvals[0], self.argvals[-1])
self.sample_range = (self.sample_points[0],
self.sample_points[-1])
else:
self.argvals_range = [(self.argvals[i][0], self.argvals[i][-1])
for i in range(len(self.argvals))]
# Default value for argvals_range is a list of tuples with the
# first and last element of each list ofthe argvals
self.sample_range = [(self.sample_points[i][0],
self.sample_points[i][-1])
for i in range(len(self.sample_points))]
# Default value for sample_range is a list of tuples with
# the first and last element of each list ofthe sample_points.
else:
self.argvals_range = argvals_range
if len(self.argvals_range) != 2:
raise ValueError("Incorrect value of argvals_range. It should"
" have two elements.")
if (self.argvals_range[0] > self.argvals[0]
or self.argvals_range[-1] < self.argvals[-1]):
self.sample_range = sample_range
if len(self.sample_range) != 2:
raise ValueError("Incorrect value of sample_range. It "
"should have two elements.")
if (self.sample_range[0] > self.sample_points[0]
or self.sample_range[-1] < self.sample_points[-1]):
raise ValueError("Timestamps must be within the time range.")

self.names = names
Expand All @@ -111,29 +114,48 @@ def round(self, decimals=0):
Returns:
:obj:FDataGrid: Returns a FDataGrid object where all elements
in its data_matrix and argvals are rounded .The real and
in its data_matrix and sample_points are rounded .The real and
imaginary parts of complex numbers are rounded separately.
"""
return FDataGrid(self.data_matrix.round(decimals),
self.argvals.round(decimals),
self.argvals_range, self.names)
self.sample_points.round(decimals),
self.sample_range, self.names)

@property
def ndim_domain(self):
""" Number of dimensions of the domain.
Returns:
int: Number of dimensions of the domain.
"""
return self.sample_points.ndim

@property
def ndim_image(self):
""" Number of dimensions of the domain.
Returns:
int: Number of dimensions of the domain.
"""
return self.data_matrix.ndim[(0,) * (1 + self.ndim_domain)]

@property
def ndim(self):
""" Number of dimensions of the data.
""" Number of dimensions of the data matrix.
Returns:
int: Number of dimensions of the data.
int: Number of dimensions of the data matrix.
"""
return self.data_matrix.ndim

@property
def nrow(self):
def n_samples(self):
""" Number of rows of the data_matrix. Also the number of samples.
Returns:
int: Number of rows of the data_matrix.
int: Number of samples of the FDataGrid object. Also the number of
rows of the data_matrix.
"""
return self.data_matrix.shape[0]
Expand Down Expand Up @@ -192,17 +214,17 @@ def derivative(self, order=1):
>>> fdata.derivative()
FDataGrid(
array([[ 1. , 1.5, 1.5, 2. , 3. ]])
,argvals=array([0, 1, 2, 3, 4])
,argvals_range=(0, 4)
,sample_points=array([0, 1, 2, 3, 4])
,sample_range=(0, 4)
,names=['Data set', 'xlabel', 'ylabel'])
Second order derivative
>>> fdata = FDataGrid([1,2,4,5,8], range(5))
>>> fdata.derivative(2)
FDataGrid(
array([[ 0.5 , 0.25, 0.25, 0.75, 1. ]])
,argvals=array([0, 1, 2, 3, 4])
,argvals_range=(0, 4)
,sample_points=array([0, 1, 2, 3, 4])
,sample_range=(0, 4)
,names=['Data set', 'xlabel', 'ylabel'])
"""
Expand All @@ -216,77 +238,94 @@ def derivative(self, order=1):
raise ValueError("The FDataGrid object cannot contain nan "
"elements.")
data_matrix = self.data_matrix
argvals = self.argvals
sample_points = self.sample_points
for _ in range(order):
mdata = []
for i in range(self.nrow):
arr = numpy.diff(data_matrix[i])/(argvals[1:] - argvals[:-1])
for i in range(self.n_samples):
arr = numpy.diff(data_matrix[i])/(sample_points[1:]
- sample_points[:-1])
arr = numpy.append(arr, arr[-1])
arr[1:-1] += arr[:-2]
arr[1:-1] /= 2
mdata.append(arr)
data_matrix = numpy.array(mdata)

return FDataGrid(data_matrix, argvals, self.argvals_range)
return FDataGrid(data_matrix, sample_points, self.sample_range)

def __add__(self, other):
if not isinstance(other, FDataGrid):
raise TypeError("Object type is not FDataGrid.")
if self.data_matrix.shape[1] != other.data_matrix.shape[1]:
raise ValueError("Error in columns dimensions")
if self.argvals != other.argvals:
raise ValueError("Error in argvals")
return FDataGrid(self.data_matrix + other.data_matrix, self.argvals,
self.argvals_range, self.names)
if self.sample_points != other.sample_points:
raise ValueError("Error in sample_points")
return FDataGrid(self.data_matrix + other.data_matrix,
self.sample_points, self.sample_range,
self.names)

def __sub__(self, other):
if not isinstance(other, FDataGrid):
raise TypeError("Object type is not FDataGrid.")
if self.data_matrix.shape[1] != other.data_matrix.shape[1]:
raise ValueError("Error in columns dimensions")
if self.argvals != other.argvals:
raise ValueError("Error in argvals")
return FDataGrid(self.data_matrix - other.data_matrix, self.argvals,
self.argvals_range, self.names)
if self.sample_points != other.sample_points:
raise ValueError("Error in sample_points")
return FDataGrid(self.data_matrix - other.data_matrix,
self.sample_points, self.sample_range,
self.names)

def __mul__(self, other):
if not isinstance(other, FDataGrid):
raise TypeError("Object type is not FDataGrid.")
if self.data_matrix.shape[1] != other.data_matrix.shape[1]:
raise ValueError("Error in columns dimensions")
if self.argvals != other.argvals:
raise ValueError("Error in argvals")
return FDataGrid(self.data_matrix * other.data_matrix, self.argvals,
self.argvals_range, self.names)
if self.sample_points != other.sample_points:
raise ValueError("Error in sample_points")
return FDataGrid(self.data_matrix * other.data_matrix,
self.sample_points, self.sample_range,
self.names)

def __truediv__(self, other):
if not isinstance(other, FDataGrid):
raise TypeError("Object type is not FDataGrid.")
if self.data_matrix.shape[1] != other.data_matrix.shape[1]:
raise ValueError("Error in columns dimensions")
if self.argvals != other.argvals:
raise ValueError("Error in argvals")
return FDataGrid(self.data_matrix / other.data_matrix, self.argvals,
self.argvals_range, self.names)
if self.sample_points != other.sample_points:
raise ValueError("Error in sample_points")
return FDataGrid(self.data_matrix / other.data_matrix,
self.sample_points, self.sample_range,
self.names)

def plot(self, *args, **kwargs):
# TODO handle names
matplotlib.pyplot.plot(self.argvals, numpy.transpose(self.data_matrix),
*args, **kwargs)
_plot = matplotlib.pyplot.plot(self.sample_points,
numpy.transpose(self.data_matrix),
*args, **kwargs)
ax = matplotlib.pyplot.gca()
ax.set_title(self.names[0])
ax.set_xlabel(self.names[1])
ax.set_ylabel(self.names[2])
return _plot

def __str__(self):
""" Return str(self). """
return ('Data set:\t' + str(self.data_matrix)
+ '\nargvals:\t' + str(self.argvals)
+ '\ntime range:\t' + str(self.argvals_range))
+ '\nsample_points:\t' + str(self.sample_points)
+ '\ntime range:\t' + str(self.sample_range))

def __repr__(self):
""" Return repr(self). """
return ("FDataGrid(\n "
+ self.data_matrix.__repr__()
+ "\n ,argvals=" + self.argvals.__repr__()
+ "\n ,argvals_range=" + self.argvals_range.__repr__()
+ "\n ,sample_points=" + self.sample_points.__repr__()
+ "\n ,sample_range=" + self.sample_range.__repr__()
+ "\n ,names=" + self.names.__repr__()
+ ")")

def __getitem__(self, key):
return FDataGrid(self.data_matrix[key], self.argvals,
self.argvals_range, self.names)
""" Return self[key]. """
if isinstance(key, tuple) and len(key) > 1:
return FDataGrid(self.data_matrix[key],
self.sample_points[key[1:1+self.ndim_domain]],
self.sample_range, self.names)
return FDataGrid(self.data_matrix[key], self.sample_points,
self.sample_range, self.names)
6 changes: 1 addition & 5 deletions fda/kernel_smoothers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,7 @@
import math

__author__ = "Miguel Carbajo Berrocal"
__license__ = "GPL3"
__version__ = ""
__maintainer__ = ""
__email__ = ""
__status__ = "Development"
__email__ = "miguel.carbajo@estudiante.uam.es"


def nw(argvals, h=None, kernel=kernels.normal, w=None, cv=False):
Expand Down
6 changes: 1 addition & 5 deletions fda/kernels.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,11 +7,7 @@


__author__ = "Miguel Carbajo Berrocal"
__license__ = "GPL3"
__version__ = ""
__maintainer__ = ""
__email__ = ""
__status__ = "Development"
__email__ = "miguel.carbajo@estudiante.uam.es"


def normal(u):
Expand Down

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