Merge branch 'master' of https://github.com/MindTraper/Python into MindTraper-master

ArithmeticAnalysis/Bisection.py

@@ -0,0 +1,33 @@
+import math
+
+
+def bisection(function, a, b):  # finds where the function becomes 0 in [a,b] using bolzano
+
+    start = a
+    end = b
+    if function(a) == 0:  # one of the a or b is a root for the function
+        return a
+    elif function(b) == 0:
+        return b
+    elif function(a) * function(b) > 0:  # if none of these are root and they are both positive or negative,
+        # then his algorithm can't find the root
+        print("couldn't find root in [a,b]")
+        return
+    else:
+        mid = (start + end) / 2
+        while abs(start - mid) > 0.0000001:  # until we achieve precise equals to 10^-7
+            if function(mid) == 0:
+                return mid
+            elif function(mid) * function(start) < 0:
+                end = mid
+            else:
+                start = mid
+            mid = (start + end) / 2
+        return mid
+
+
+def f(x):
+    return math.pow(x, 3) - 2*x - 5
+
+
+print(bisection(f, 1, 1000))

ArithmeticAnalysis/Intersection.py

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+import math
+
+def intersection(function,x0,x1): #function is the f we want to find its root and x0 and x1 are two random starting points
+    x_n = x0
+    x_n1 = x1
+    while True:
+        x_n2 = x_n1-(function(x_n1)/((function(x_n1)-function(x_n))/(x_n1-x_n)))
+        if abs(x_n2 - x_n1)<0.00001 :
+            return x_n2
+        x_n=x_n1
+        x_n1=x_n2
+
+def f(x):
+    return math.pow(x,3)-2*x-5
+
+print(intersection(f,3,3.5))

ArithmeticAnalysis/LUdecomposition.py

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+import math
+import numpy
+
+def LUDecompose (table): #table that contains our data
+    #table has to be a square array so we need to check first
+    rows,columns=numpy.shape(table)
+    L=numpy.zeros((rows,columns))
+    U=numpy.zeros((rows,columns))
+    if rows!=columns:
+        return
+    for i in range (columns):
+        for j in range(i-1):
+            sum=0
+            for k in range (j-1):
+                sum+=L[i][k]*U[k][j]
+            L[i][j]=(table[i][j]-sum)/U[j][j]
+        L[i][i]=1
+        for j in range(i-1,columns):
+            sum1=0
+            for k in range(i-1):
+                sum1+=L[i][k]*U[k][j]
+            U[i][j]=table[i][j]-sum1
+    return L,U
+
+
+
+
+
+
+
+matrix =numpy.array([[2,-2,1],[0,1,2],[5,3,1]])
+L,U = LUDecompose(matrix)
+print(L)
+print(U)

ArithmeticAnalysis/NeutonMethod.py

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+def newton(function,function1,startingInt): #function is the f(x) and function1 is the f'(x)
+  x_n=startingInt
+  while True:
+      x_n1=x_n-function(x_n)/function1(x_n)
+      if abs(x_n-x_n1)<0.00001:
+          return x_n1
+      x_n=x_n1
+      
+def f(x):
+    return (x**3)-2*x-5
+
+def f1(x):
+    return 3*(x**2)-2
+
+print(newton(f,f1,3))