def _init_matrix(self, values, index, columns, dtype):
if not isinstance(values, np.ndarray):
arr = np.array(values)
if issubclass(arr.dtype.type, basestring):
arr = np.array(values, dtype=object, copy=True)
values = arr
if values.ndim == 1:
N = values.shape[0]
if N == 0:
values = values.reshape((values.shape[0], 0))
else:
values = values.reshape((values.shape[0], 1))
if dtype is not None:
try:
values = values.astype(dtype)
except Exception:
pass
N, K = values.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
return index, columns, values
def _init_matrix(self, values, index, columns, dtype):
if not isinstance(values, np.ndarray):
arr = np.array(values)
if issubclass(arr.dtype.type, basestring):
arr = np.array(values, dtype=object, copy=True)
values = arr
if values.ndim == 1:
N = values.shape[0]
if N == 0:
values = values.reshape((values.shape[0], 0))
else:
values = values.reshape((values.shape[0], 1))
if dtype is not None:
try:
values = values.astype(dtype)
except Exception:
pass
N, K = values.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
return index, columns, values
def _prep_index(self, data, index, columns):
N, K = data.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
if len(columns) != K:
raise ValueError('Column length mismatch: %d vs. %d' %
(len(columns), K))
if len(index) != N:
raise ValueError('Index length mismatch: %d vs. %d' %
(len(index), N))
return index, columns
def _prep_index(self, data, index, columns):
N, K = data.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
if len(columns) != K:
raise ValueError('Column length mismatch: %d vs. %d' %
(len(columns), K))
if len(index) != N:
raise ValueError('Index length mismatch: %d vs. %d' %
(len(index), N))
return index, columns
def _prep_index(self, data, index, columns):
N, K = data.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
if len(columns) != K:
raise ValueError('Column length mismatch: {columns} vs. {K}'
.format(columns=len(columns), K=K))
if len(index) != N:
raise ValueError('Index length mismatch: {index} vs. {N}'
.format(index=len(index), N=N))
return index, columns
def _prep_index(self, data, index, columns):
N, K = data.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
if len(columns) != K:
raise ValueError('Column length mismatch: {columns} vs. {K}'
.format(columns=len(columns), K=K))
if len(index) != N:
raise ValueError('Index length mismatch: {index} vs. {N}'
.format(index=len(index), N=N))
return index, columns
def _init_matrix(self, data, index, columns, dtype=None):
data = _prep_ndarray(data, copy=False)
N, K = data.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
if len(columns) != K:
raise ValueError("Column length mismatch: %d vs. %d" % (len(columns), K))
if len(index) != N:
raise ValueError("Index length mismatch: %d vs. %d" % (len(index), N))
data = dict([(idx, data[:, i]) for i, idx in enumerate(columns)])
return self._init_dict(data, index, columns, dtype)
def _init_matrix(self, data, index, columns, dtype=None):
data = _prep_ndarray(data, copy=False)
N, K = data.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
if len(columns) != K:
raise ValueError('Column length mismatch: %d vs. %d' %
(len(columns), K))
if len(index) != N:
raise ValueError('Index length mismatch: %d vs. %d' %
(len(index), N))
data = dict([(idx, data[:, i]) for i, idx in enumerate(columns)])
return self._init_dict(data, index, columns, dtype)
