gauss more efficient

Source Code - Seals/process/process.py

@@ -44,48 +44,42 @@ def identity(matrix):
         
     return matrix
 
-def gauss(matrix):
+def gauss(a):
     
-    i = 0
-    k = 0
+    for i in range(a.shape[0]):
 
-    while (i < matrix.shape[0]):
+        k = i + 1
 
-        if (matrix[i][i] == 0):
+        while (k<a.shape[0]):
 
-            n = i
+            l = 1
+            while (a[i][i] == 0 and l < a.shape[0]):
 
-            while (matrix[i][i] == 0) and (n < matrix.shape[0]):
+                temp = a[i].copy()
+                a[i] = a[i+l]
+                a[i+l] = temp
 
-                temp = matrix[i].copy()
-                matrix[i] = matrix[n]
-                matrix[n] = temp
+                l += 1
             
-                n += 1
+            while (a[k][i] == 0 and (k+1) < a.shape[0]):
+                k += 1
 
-        while (k < matrix.shape[0]):
-            
-            if (k == i) or (matrix[i][i] == 0):
+            a[k] = a[k] - (a[k][i]/a[i][i])*a[i]
             
             k += 1
 
-            else:
-                
-                mult = matrix[k][i]/matrix[i][i]
-                matrix[k] = matrix[k] - mult*matrix[i]
-                k += 1
-            
-        i += 1
-        k = 0
-    
-    i = 0
-    
-    while ((i) < matrix.shape[0]) and (matrix[i][i] != 0):
-        
-        matrix[i] = matrix[i]/matrix[i][i]
         i += 1
 
-    return matrix[:,(matrix.shape[0]):]
+    x = np.zeros(a.shape[0])
+
+    for j in reversed(range(x.shape[0])):
+        
+        suma = 0
+        for k in range (j,a.shape[0]):
+            suma += a[j][k]*x[k]
+        x[j] = (a[j][a.shape[0]] - suma)/a[j][j]
+    
+    return x
 
 def inverse(matrix):