Merge branch 'master' into master

mathgenerator/funcs/__init__.py

@@ -71,5 +71,11 @@ from .multiplyComplexNumbersFunc import *
 from .geomProgrFunc import *
 from .geometricMeanFunc import *
 from .harmonicMeanFunc import *
+from .euclidianNormFunc import *
+from .angleBtwVectorsFunc import *
+from .absoluteDifferenceFunc import *
+from .vectorDotFunc import *
+from .binary2sComplement import *
+from .matrixInversion import *
 from .sectorAreaFunc import*
 from .meanMedianFunc import*

mathgenerator/funcs/absoluteDifferenceFunc.py

@@ -0,0 +1,10 @@
+from  .__init__ import *
+
+def absoluteDifferenceFunc (maxA = 100, maxB = 100):
+	a = random.randint(-1*maxA, maxA)
+	b = random.randint(-1*maxB, maxB)
+	absDiff = abs(a-b)
+
+	problem = "Absolute difference between numbers " + str(a) + " and " + str(b) + " = "
+	solution = absDiff
+	return problem, solution

mathgenerator/funcs/angleBtwVectorsFunc.py

@@ -0,0 +1,16 @@
+from .euclidianNormFunc import euclidianNormFunc
+import math
+from .__init__ import *
+
+
+def angleBtwVectorsFunc(v1: list, v2: list):
+    sum = 0
+    for i in v1:
+        for j in v2:
+            sum += i * j
+
+    mags = euclidianNormFunc(v1) * euclidianNormFunc(v2)
+    problem = f"angle between the vectors {v1} and {v2} is:"
+    solution = math.acos(sum / mags)
+    # would return the answer in radians
+    return problem, solution

mathgenerator/funcs/binary2sComplement.py

@@ -0,0 +1,26 @@
+from  .__init__ import *
+
+def binary2sComplementFunc(maxDigits=10):
+    digits = random.randint(1, maxDigits)
+    question = ''.join([str(random.randint(0, 1)) for i in range(digits)]).lstrip('0')
+
+    answer = []
+    for i in question:
+        answer.append(str(int(not bool(int(i)))))
+
+    carry = True
+    j = len(answer) - 1
+    while j >= 0:
+        if answer[j] == '0':
+            answer[j] = '1'
+            carry = False
+            break
+        answer[j] = '0'
+        j -= 1
+
+    if j == 0 and carry == True:
+        answer.insert(0, '1')
+
+    problem = "2's complement of " + question + " ="
+    solution = ''.join(answer).lstrip('0')
+    return problem, solution

mathgenerator/funcs/euclidianNormFunc.py

@@ -0,0 +1,7 @@
+from .__init__ import *
+
+
+def euclidianNormFunc(v1: list):
+    problem = f"Euclidian norm or L2 norm of the vector{v1} is:"
+    solution = sqrt(sum([i**2 for i in v1]))
+    return problem, solution

mathgenerator/funcs/matrixInversion.py

@@ -0,0 +1,69 @@
+from .__init__ import *
+import sympy
+
+def matrixInversion(SquareMatrixDimension=3, MaxMatrixElement=99, OnlyIntegerElementsInInvertedMatrix=False):
+    if OnlyIntegerElementsInInvertedMatrix is True:
+        isItOk = False
+        Mat = list()
+        while (isItOk is False):
+            Mat = list()
+            for i in range(0, SquareMatrixDimension):
+                z = list()
+                for j in range(0, SquareMatrixDimension):
+                    z.append(0)
+                z[i] = 1
+                Mat.append(z)
+            MaxAllowedMatrixElement = math.ceil(
+                pow(MaxMatrixElement, 1 / (SquareMatrixDimension)))
+            randomlist = random.sample(
+                range(0, MaxAllowedMatrixElement + 1), SquareMatrixDimension)
+
+            for i in range(0, SquareMatrixDimension):
+                if i == SquareMatrixDimension - 1:
+                    Mat[0] = [j + (k * randomlist[i])
+                              for j, k in zip(Mat[0], Mat[i])]
+                else:
+                    Mat[i + 1] = [j + (k * randomlist[i])
+                                  for j, k in zip(Mat[i + 1], Mat[i])]
+
+            for i in range(1, SquareMatrixDimension - 1):
+                Mat[i] = [sum(i)
+                          for i in zip(Mat[SquareMatrixDimension - 1], Mat[i])]
+
+            isItOk = True
+            for i in Mat:
+                for j in i:
+                    if j > MaxMatrixElement:
+                        isItOk = False
+                        break
+                if isItOk is False:
+                    break
+
+        random.shuffle(Mat)
+        Mat = sympy.Matrix(Mat)
+        Mat = sympy.Matrix.transpose(Mat)
+        Mat = Mat.tolist()
+        random.shuffle(Mat)
+        Mat = sympy.Matrix(Mat)
+        Mat = sympy.Matrix.transpose(Mat)
+
+    else:
+        randomlist = list(sympy.primerange(0, MaxMatrixElement + 1))
+        plist = random.sample(randomlist, SquareMatrixDimension)
+        randomlist = random.sample(
+            range(0, MaxMatrixElement + 1), SquareMatrixDimension * SquareMatrixDimension)
+        randomlist = list(set(randomlist) - set(plist))
+        n_list = random.sample(
+            randomlist, SquareMatrixDimension * (SquareMatrixDimension - 1))
+        Mat = list()
+        for i in range(0, SquareMatrixDimension):
+            z = list()
+            z.append(plist[i])
+            for j in range(0, SquareMatrixDimension - 1):
+                z.append(n_list[(i * SquareMatrixDimension) + j - i])
+            random.shuffle(z)
+            Mat.append(z)
+        Mat = sympy.Matrix(Mat)
+    problem = 'Inverse of Matrix ' + str(Mat) + ' is:'
+    solution = str(sympy.Matrix.inv(Mat))
+    return problem, solution

mathgenerator/funcs/matrixMultiplicationFunc.py

@@ -40,11 +40,12 @@ def matrixMultiplicationFuncHelper(inp):
     m = len(inp)
     n = len(inp[0])
 
-    string = ""
+    string = "[["
     for i in range(m):
         for j in range(n):
             string += f"{inp[i][j]: 6d}"
-            string += "  "
-        string += "\n"
+            string += ", "if j < n-1 else ""
+        string += "]\n [" if i < m-1 else ""
+    string += "]]"
         
     return string

mathgenerator/funcs/moduloFunc.py

@@ -4,7 +4,7 @@ from .__init__ import *
 def moduloFunc(maxRes=99, maxModulo=99):
     a = random.randint(0, maxModulo)
     b = random.randint(0, min(maxRes, maxModulo))
-    c = a % b
+    c = a % b if b != 0 else 0
     
     problem = str(a) + "%" + str(b) + "="
     solution = str(c)

mathgenerator/funcs/profitLossPercentFunc.py

@@ -12,3 +12,5 @@ def profitLossPercentFunc(maxCP = 1000, maxSP = 1000):
     percent = diff/cP * 100
     problem = f"{profitOrLoss} percent when CP = {cP} and SP = {sP} is: "
     solution = percent
+    
+    return problem, solution

mathgenerator/funcs/vectorDotFunc.py

@@ -0,0 +1,11 @@
+from .__init__ import *
+
+
+def vectorDotFunc(minVal=-20, maxVal=20):
+     a = [random.randint(minVal, maxVal) for i in range(3)]
+     b = [random.randint(minVal, maxVal) for i in range(3)]
+     c = a[0] * b[0] +  a[1] * b[1] + a[2] * b[2] 
+
+     problem = str(a) + " . " + str(b) + " = "
+     solution = str(c)
+     return problem, solution