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Update utils.py #43

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14 changes: 7 additions & 7 deletions mnnpy/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,17 +11,17 @@



@jit(float32[:](float32[:, :]), nogil=True)

def l2_norm(in_matrix):
return np.linalg.norm(x=in_matrix, axis=1)


@jit(float32[:, :](float32[:, :], float32[:, :]), nogil=True)

def scale_rows(in_matrix, scale_vector):
return np.divide(in_matrix, scale_vector)


@jit(float32[:, :](float32[:, :], float32[:, :]))

def kdist(m, n):
dist = np.zeros((m.shape[0], n.shape[0]), dtype=np.float32)
for i in range(m.shape[0]):
Expand Down Expand Up @@ -85,7 +85,7 @@ def transform_input_data(datas, cos_norm_in, cos_norm_out, var_index, var_subset
return in_batches, out_batches, var_sub_index, same_set


@jit((float32[:, :], float32[:, :], int8, int8, int8))

def find_mutual_nn(data1, data2, k1, k2, n_jobs):
k_index_1 = cKDTree(data1).query(x=data2, k=k1, n_jobs=n_jobs)[1]
k_index_2 = cKDTree(data2).query(x=data1, k=k2, n_jobs=n_jobs)[1]
Expand All @@ -99,7 +99,7 @@ def find_mutual_nn(data1, data2, k1, k2, n_jobs):
return mutual_1, mutual_2


@jit(float32[:, :](float32[:, :], float32[:, :], int32[:], int32[:], float32[:, :], float32))

def compute_correction(data1, data2, mnn1, mnn2, data2_or_raw2, sigma):
vect = data1[mnn1] - data2[mnn2]
mnn_index, mnn_count = np.unique(mnn2, return_counts=True)
Expand Down Expand Up @@ -196,7 +196,7 @@ def adjust_shift_variance(data1, data2, correction, sigma, n_jobs, var_subset=No
return correction * scaling[:, None]


@jit(float32(float32[:, :], float32[:, :], float32[:], float32[:], float32), nogil=True)

def adjust_s_variance(data1, data2, curcell, curvect, sigma):
distance1 = np.zeros((data1.shape[0], 2), dtype=np.float32)
l2_norm = np.linalg.norm(curvect)
Expand Down Expand Up @@ -232,7 +232,7 @@ def adjust_s_variance(data1, data2, curcell, curvect, sigma):
return (ref_quan - curproj) / l2_norm


@jit(float32(float32[:], float32[:], float32[:]), nopython=True)

def sq_dist_to_line(ref, grad, point):
working = ref - point
scale = np.dot(working, grad)
Expand Down