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PATCH: np int conversion #60

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PATCH: np int conversion #60

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6 changes: 3 additions & 3 deletions telescope/utils/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -676,7 +676,7 @@ def __init__(self, score_matrix, opts):
# Y[i] is the ambiguity indicator for fragment i, where Y[i]=1 if
# fragment i is aligned to multiple transcripts and Y[i]=0 otherwise.
# Store as N x 1 matrix
self.Y = (self.Q.count(1) > 1).astype(np.uint8)
self.Y = (self.Q.count(1) > 1).astype(int)
self._yslice = self.Y[:,0].nonzero()[0]

# Log-likelihood score
Expand Down Expand Up @@ -856,10 +856,10 @@ def reassign(self, method, thresh=0.9, initial=False):
assignments = v.norm(1)
elif method == 'unique':
# Zero all rows that are ambiguous
assignments = _z.multiply(1 - self.Y).ceil().astype(np.uint8)
assignments = _z.multiply(1 - self.Y).ceil().astype(int)
elif method == 'all':
# Return all nonzero elements
assignments = _z.apply_func(lambda x: 1 if x > 0 else 0).astype(np.uint8)
assignments = _z.apply_func(lambda x: 1 if x > 0 else 0).astype(int)

assignments = csr_matrix(assignments)
return assignments
Expand Down
6 changes: 3 additions & 3 deletions telescope/utils/sparse_plus.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,13 +53,13 @@ def _norm(self, axis=None):

def _norm_loop(self, axis=None):
if axis is None:
ret = self.copy().astype(np.float)
ret = self.copy().astype(float)
ret.data /= sum(ret)
return ret
elif axis == 0:
raise NotImplementedError
elif axis == 1:
ret = self.copy().astype(np.float)
ret = self.copy().astype(float)
rowiter = zip(ret.indptr[:-1], ret.indptr[1:], ret.sum(1).A1)
for d_start, d_end, d_sum in rowiter:
if d_sum != 0:
Expand Down Expand Up @@ -89,7 +89,7 @@ def scale(self, axis=None):
def _scale(self, axis=None):
if axis is None:
return type(self)(self.multiply(1. / self.max()))
# ret = self.copy().astype(np.float)
# ret = self.copy().astype(float)
# return ret / ret.max()
elif axis == 0:
raise NotImplementedError
Expand Down