def read(self):
who = matlab.whosmat(self.filename)
if not who:
raise IOError("Couldn't load matlab file " + self.filename)
else:
ml = matlab.loadmat(self.filename, chars_as_strings=True)
ml = {a: b for a, b in ml.items() if isinstance(b, np.ndarray)}
# X is the biggest numeric array
numarrays = []
for name, con in ml.items():
if issubclass(con.dtype.type, numbers.Number):
numarrays.append(
(name, reduce(lambda x, y: x * y, con.shape, 1)))
X = None
if numarrays:
nameX = max(numarrays, key=lambda x: x[1])[0]
X = ml.pop(nameX)
# find an array with compatible shapes
attributes = []
if X is not None:
nameattributes = None
for name, con in ml.items():
if con.shape in [(X.shape[1], ), (1, X.shape[1])]:
nameattributes = name
break
attributenames = ml.pop(nameattributes).ravel(
) if nameattributes else range(X.shape[1])
attributenames = [str(a).strip() for a in attributenames
] # strip because of numpy char array
attributes = [
ContinuousVariable.make(a) for a in attributenames
]
metas = []
metaattributes = []
sizemetas = None
if X is None:
counts = defaultdict(list)
for name, con in ml.items():
counts[len(con)].append(name)
if counts:
sizemetas = max(counts.keys(),
key=lambda x: len(counts[x]))
else:
sizemetas = len(X)
if sizemetas:
for name, con in ml.items():
if len(con) == sizemetas:
metas.append(name)
metadata = []
for m in sorted(metas):
f = ml[m]
metaattributes.append(StringVariable.make(m))
f.resize(sizemetas, 1)
metadata.append(f)
metadata = np.hstack(tuple(metadata))
domain = Domain(attributes, metas=metaattributes)
if X is None:
X = np.zeros((sizemetas, 0))
return Orange.data.Table.from_numpy(domain,
X,
Y=None,
metas=metadata)
def read(self):
who = matlab.whosmat(self.filename)
if not who:
raise IOError("Couldn't load matlab file " + self.filename)
else:
ml = matlab.loadmat(self.filename, chars_as_strings=True)
ml = {a: b for a, b in ml.items() if isinstance(b, np.ndarray)}
def num_elements(array):
return reduce(lambda x, y: x * y, array.shape, 1)
def find_biggest(arrays):
sizes = []
for n, c in arrays.items():
sizes.append((num_elements(c), n))
return max(sizes)[1]
def is_string_array(array):
return issubclass(array.dtype.type, np.str_)
def is_number_array(array):
return issubclass(array.dtype.type, numbers.Number)
numeric = {n: a for n, a in ml.items() if is_number_array(a)}
# X is the biggest numeric array
X = ml.pop(find_biggest(numeric)) if numeric else None
# find an array with compatible shapes
attributes = []
if X is not None:
name_array = None
for name in sorted(ml):
con = ml[name]
if con.shape in [(X.shape[1],), (1, X.shape[1])]:
name_array = name
break
names = ml.pop(name_array).ravel() if name_array else range(X.shape[1])
names = [str(a).rstrip() for a in names] # remove matlab char padding
attributes = [ContinuousVariable.make(a) for a in names]
meta_names = []
metas = []
meta_size = None
if X is None:
counts = defaultdict(list)
for name, con in ml.items():
counts[len(con)].append(name)
if counts:
meta_size = max(counts.keys(), key=lambda x: len(counts[x]))
else:
meta_size = len(X)
if meta_size:
for name, con in ml.items():
if len(con) == meta_size:
meta_names.append(name)
meta_data = []
for m in sorted(meta_names):
f = ml[m]
if is_string_array(f) and len(f.shape) == 1: # 1D string arrays
metas.append(StringVariable.make(m))
f = np.array([a.rstrip() for a in f]) # remove matlab char padding
f.resize(meta_size, 1)
meta_data.append(f)
elif is_number_array(f) and len(f.shape) == 2:
if f.shape[1] == 1:
names = [m]
else:
names = [m + "_" + str(i+1) for i in range(f.shape[1])]
for n in names:
metas.append(ContinuousVariable.make(n))
meta_data.append(f)
meta_data = np.hstack(tuple(meta_data)) if meta_data else None
domain = Domain(attributes, metas=metas)
if X is None:
X = np.zeros((meta_size, 0))
return Orange.data.Table.from_numpy(domain, X, Y=None, metas=meta_data)
def read(self):
who = matlab.whosmat(self.filename)
if not who:
raise IOError("Couldn't load matlab file " + self.filename)
else:
ml = matlab.loadmat(self.filename, chars_as_strings=True)
ml = {a: b for a, b in ml.items() if isinstance(b, np.ndarray)}
def num_elements(array):
return reduce(lambda x, y: x * y, array.shape, 1)
def find_biggest(arrays):
sizes = []
for n, c in arrays.items():
sizes.append((num_elements(c), n))
return max(sizes)[1]
def is_string_array(array):
return issubclass(array.dtype.type, np.str_)
def is_number_array(array):
return issubclass(array.dtype.type, numbers.Number)
numeric = {n: a for n, a in ml.items() if is_number_array(a)}
# X is the biggest numeric array
X = ml.pop(find_biggest(numeric)) if numeric else None
# find an array with compatible shapes
attributes = []
if X is not None:
name_array = None
for name in sorted(ml):
con = ml[name]
if con.shape in [(X.shape[1], ), (1, X.shape[1])]:
name_array = name
break
names = ml.pop(name_array).ravel() if name_array else range(
X.shape[1])
names = [str(a).rstrip()
for a in names] # remove matlab char padding
attributes = [ContinuousVariable.make(a) for a in names]
meta_names = []
metas = []
meta_size = None
if X is None:
counts = defaultdict(list)
for name, con in ml.items():
counts[len(con)].append(name)
if counts:
meta_size = max(counts.keys(),
key=lambda x: len(counts[x]))
else:
meta_size = len(X)
if meta_size:
for name, con in ml.items():
if len(con) == meta_size:
meta_names.append(name)
meta_data = []
for m in sorted(meta_names):
f = ml[m]
if is_string_array(f) and len(
f.shape) == 1: # 1D string arrays
metas.append(StringVariable.make(m))
f = np.array([a.rstrip()
for a in f]) # remove matlab char padding
f.resize(meta_size, 1)
meta_data.append(f)
elif is_number_array(f) and len(f.shape) == 2:
if f.shape[1] == 1:
names = [m]
else:
names = [
m + "_" + str(i + 1) for i in range(f.shape[1])
]
for n in names:
metas.append(ContinuousVariable.make(n))
meta_data.append(f)
meta_data = np.hstack(tuple(meta_data)) if meta_data else None
domain = Domain(attributes, metas=metas)
if X is None:
X = np.zeros((meta_size, 0))
return Orange.data.Table.from_numpy(domain,
X,
Y=None,
metas=meta_data)
def read(self):
who = matlab.whosmat(self.filename)
if not who:
raise IOError("Couldn't load matlab file " + self.filename)
else:
ml = matlab.loadmat(self.filename, chars_as_strings=True)
ml = {a: b for a, b in ml.items() if isinstance(b, np.ndarray)}
# X is the biggest numeric array
numarrays = []
for name, con in ml.items():
if issubclass(con.dtype.type, numbers.Number):
numarrays.append((name, reduce(lambda x, y: x*y, con.shape, 1)))
X = None
if numarrays:
nameX = max(numarrays, key=lambda x: x[1])[0]
X = ml.pop(nameX)
# find an array with compatible shapes
attributes = []
if X is not None:
nameattributes = None
for name, con in ml.items():
if con.shape in [(X.shape[1],), (1, X.shape[1])]:
nameattributes = name
break
attributenames = ml.pop(nameattributes).ravel() if nameattributes else range(X.shape[1])
attributenames = [str(a).strip() for a in attributenames] # strip because of numpy char array
attributes = [ContinuousVariable.make(a) for a in attributenames]
metas = []
metaattributes = []
sizemetas = None
if X is None:
counts = defaultdict(list)
for name, con in ml.items():
counts[len(con)].append(name)
if counts:
sizemetas = max(counts.keys(), key=lambda x: len(counts[x]))
else:
sizemetas = len(X)
if sizemetas:
for name, con in ml.items():
if len(con) == sizemetas:
metas.append(name)
metadata = []
for m in sorted(metas):
f = ml[m]
metaattributes.append(StringVariable.make(m))
f.resize(sizemetas, 1)
metadata.append(f)
metadata = np.hstack(tuple(metadata))
domain = Domain(attributes, metas=metaattributes)
if X is None:
X = np.zeros((sizemetas, 0))
return Orange.data.Table.from_numpy(domain, X, Y=None, metas=metadata)