v1.3.1

Older Versions/yoshi-seals1.3/Seals/process/process.py

@@ -0,0 +1,203 @@
+# Seals - Program made for educational intent, can be freely distributed
+# and can be used for economical intent. I will not take legal actions
+# unless my intelectual propperty, the code, is stolen or change without permission.  
+
+# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
+
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License version 2 as published by
+# the Free Software Foundation.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+
+import numpy as np
+import math
+
+def identity(matrix):
+    
+    i = 0
+    
+    while (i < matrix.shape[0]):
+        
+        j = 0
+        
+        while (j < matrix.shape[0]):
+            
+            if (i == j):
+                
+                matrix[i][j] = 1
+                
+            elif (i != j):
+                
+                matrix[i][j] = 0
+                        
+            j += 1
+        
+        i += 1
+        
+    return matrix
+
+def gauss(matrix):
+    
+    i = 0
+    k = 0
+    
+    while (i < matrix.shape[0]):
+        
+        if (matrix[i][i] == 0):
+            
+            n = i
+            
+            while (matrix[i][i] == 0) and (n < matrix.shape[0]):
+                
+                temp = matrix[i].copy()
+                matrix[i] = matrix[n]
+                matrix[n] = temp
+                
+                n += 1
+        
+        while (k < matrix.shape[0]):
+            
+            if (k == i) or (matrix[i][i] == 0):
+                
+                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]):]
+
+def inverse(matrix):
+    
+    return gauss(np.hstack((matrix, identity(np.zeros(matrix.shape)))))
+
+def cholesky(A, b):
+    
+    g = np.zeros((A.shape))
+    
+    i = 0
+    j = 0
+    
+    while j < A.shape[1]:
+        while i < A.shape[0]:
+
+            if i == 0 and j == 0:
+
+                g[i][j] = math.sqrt(A[0][0])
+
+            elif j == 0:
+
+                g[i][j] = A[i][0]/g[0][0]
+
+            elif i == j:
+
+                k = 0
+                theta = 0
+
+                while k < i:
+
+                    theta += g[i][k]**2
+                    k += 1
+                
+                g[i][j] = math.sqrt(A[i][i] - theta) 
+
+            else:
+
+                k = 0
+                theta = 0
+
+                while k < j:
+
+                    theta += g[i][k]*g[j][k]
+                    k += 1
+
+                g[i][j] = (A[i][j] - theta)/g[j][j]
+
+            i += 1
+
+        j += 1
+        i = j
+                
+    y = (inverse(g)).dot(b) 
+    
+    x = (inverse(g.T)).dot(y)
+    
+    return x
+
+def decomposition(U, b):
+    
+    L = identity(np.zeros(U.shape))
+
+    i = 0
+    k = 0
+
+    while (i < U.shape[0]):
+        
+        k = 0
+
+        if (U[i][i] == 0):
+            
+            n = i
+            
+            while (U[i][i] == 0) and (n < U.shape[0]):
+                
+                temp = U[i].copy()
+                U[i] = U[n]
+                U[n] = temp
+                
+                n += 1
+        
+        while (k < U.shape[0]):
+            
+            if (k <= i) or (U[i][i] == 0):
+                
+                k += 1
+                
+            else:
+                L[k][i] = U[k][i]/U[i][i]
+                U[k] = U[k] - L[k][i]*U[i]
+                k += 1
+            
+        i += 1
+
+    y = (inverse(L)).dot(b) 
+    
+    x = (inverse(U)).dot(y)
+    
+    return x
+    
+def cramer(A, b):
+
+    x = np.vstack(np.zeros(b.shape))
+    k = 0
+
+    while (k < A.shape[0]):
+
+        temp = A.copy()
+        temp[:,k] = b
+
+        x[k] = np.linalg.det(temp)/np.linalg.det(A)
+        
+        k += 1
+    
+    return x