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src/py/crankshaft/crankshaft/regression/glm/iwls.py

@@ -49,7 +49,7 @@ def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=
     if isinstance(family, Binomial):
         y = family.link._clean(y)
     if isinstance(family, Poisson):
-    	y_off = y/offset
+        y_off = y/offset
         y_off = family.starting_mu(y_off)
         v = family.predict(y_off)
         mu = family.starting_mu(y)
@@ -58,13 +58,13 @@ def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=
         v = family.predict(mu)
     
     while diff > tol and n_iter < max_iter:
-    	n_iter += 1
+        n_iter += 1
         w = family.weights(mu)
         z = v + (family.link.deriv(mu)*(y-mu))
         w = np.sqrt(w)
         if type(x) != np.ndarray:
-        	w = sp.csr_matrix(w)
-        	z = sp.csr_matrix(z)
+            w = sp.csr_matrix(w)
+            z = sp.csr_matrix(z)
         wx = spmultiply(x, w, array_out=False)
         wz = spmultiply(z, w, array_out=False)
         if wi is None:

src/py/crankshaft/crankshaft/regression/gwr/base/search.py

@@ -47,14 +47,14 @@ def golden_section(a, c, delta, function, tol, max_iter, int_score=False):
     while np.abs(diff) > tol and iters < max_iter:
         iters += 1
         if int_score:
-        	b = np.round(b)
-        	d = np.round(d)
+            b = np.round(b)
+            d = np.round(d)
 
         score_a = function(a)
         score_b = function(b)
         score_c = function(c)
         score_d = function(d)
-        
+
         if score_b <= score_d:
             opt_val = b
             opt_score = score_b
@@ -73,7 +73,7 @@ def golden_section(a, c, delta, function, tol, max_iter, int_score=False):
                 #d = np.round(b)
         
         #if int_score:
-        #	opt_val = np.round(opt_val)
+        #    opt_val = np.round(opt_val)
         output.append((opt_val, opt_score))
         diff = score_b - score_d
         score = opt_score