First Commit

.gitignore

+# Original file copied From: https://gist.github.com/kjaanson/de713cff4ab7d493563fd9731a232534
+/*.egg-info
+/.spyderproject
+.spyderproject
+.idea/*
+/**/.DS_Store
+/**/__pycache__
+/**/.ipynb_checkpoints
+/.doit.db.bak
+/.doit.db.dat
+/.doit.db.dir
+*.mp4
+*.mpg
+*.html

answercheck.py

+import hashlib
+import numpy as np
+import sympy as sym
+import sys
+import textwrap
+
+
+# detailedwarnings = True
+
+# Things I fixed. Fixed Matrix rounding error
+# Added more print warnings
+
+# TODO: Fix Printwarnings
+
+
+def printwarning(message):
+    if checkanswer.detailedwarnings:
+        print(message)
+
+
+class checkanswer():
+    detailedwarnings = True
+
+    def __init__(self, var, hashtag=None):
+        checkanswer.basic(var, hashtag)
+
+    def basic(var, hashtag=None):
+        """Fuanction that encodes answers in a string called a Hash.
+        This is a one way function so a correct answer will generate the
+        correct has. An incorrect answer will generate an incorrect hash."""
+
+        if checkanswer.detailedwarnings:
+            print(f"Testing {var}")
+        else:
+            print(f"Testing Answer")
+            
+        curr_printopts = np.get_printoptions()
+        np.set_printoptions(threshold=sys.maxsize)
+        varstr = f"{var}"
+        np.set_printoptions(threshold=curr_printopts['threshold'])
+
+        t2 = varstr.encode("utf-8")
+        m = hashlib.md5(t2)
+        checktag = m.hexdigest()
+        if hashtag:
+            if checktag == hashtag:
+                print("Answer seems to be correct\n")
+            else:
+                print("Answer seems to be incorrect\n")
+                assert checktag == hashtag, f"Answer is incorrect {checktag}"
+        else:
+            raise TypeError(f"No answer hastag provided: {checktag}")
+
+    def float(A, hashtag=None, decimal_accuracy = 5):
+        """Function to check matrix type before hashing."""
+        if(type(A) is not float):
+            if(type(A) is list):
+                printwarning(textwrap.dedent(f"""
+                CheckWarning: passed variable is a list and not a float...
+                    Cannot convert list to float directly. We will assume this
+                    list has only one element and covert to a numpy matrix
+                    using ```A = np.matrix(A)```.\n"""))
+                A = np.matrix(A)
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: passed variable is {type(A)} and not a float.
+                Trying to convert to a float using ```A = float(A)```.\n"""))
+            A = float(A)
+        A = np.round(A, decimals=decimal_accuracy)
+        if A == -0.00:
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: Value is negative zero...
+                Converting to positive zero before checking using ```A = 0.00```.\n"""))
+            A = 0.00
+        return checkanswer.basic(A, hashtag)
+
+    def make_vector(A, decimal_accuracy = 5):
+        """Function to check matrix type before hashing."""
+        if(type(A) is not np.matrix):
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: passed variable is {type(A)} and not a numpy.matrix.
+                Trying to convert to a array matrix using ```A = np.matrix(A)```.\n"""))
+            A = np.matrix(A)
+        if not np.issubdtype(A.dtype, np.dtype(float).type):
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: passed matrix is {A.dtype} and not {np.dtype(float).type}...
+                Trying to convert to float using ```A = A.astype(float)```.\n"""))
+            A = A.astype(float)
+        if(A.shape[0] != 1 and A.shape[1] != 1):
+            assert A.shape[0] != 1 and A.shape[1] != 1, \
+                f"Matrix is not of vector format {A}"
+        if(A.shape[0] != 1):
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: numpy.matrix is row vector...
+                Trying to convert to a column vector using ```A = A.T```.\n"""))
+            A = A.T
+        A = np.round(A, decimals=decimal_accuracy)
+        if not A[A == -0].size == 0:
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: Vector contains negative values for zero...
+                Converting to positive values of zero using  ```A[A==-0] = 0```.\n"""))
+            A[A == -0] = 0.00
+        return A
+    
+    def vector(A, hashtag=None, decimal_accuracy = 5):
+        A = checkanswer.make_vector(A, decimal_accuracy)
+        return checkanswer.basic(A, hashtag)
+    
+    def eq_vector(A, hashtag=None, decimal_accuracy = 5):
+        A = checkanswer.make_vector(A, decimal_accuracy)
+        vecsum = np.sqrt(np.sum(np.dot(A,A.T)))
+        if not vecsum == 1:
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: Vector sum of {A} has total value of {vecsum}...
+                Trying to normalize to unit vector to check answer using
+                using ```A = A/{vecsum}```.\n\n"""))
+            A = A/vecsum
+        if(A[0, 0] < 0):
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: First element of {A} is negative ({A[0,0]}.
+                Trying to normalize by making this value positive using ```A = -A```.\n"""))
+            A = -A
+        A = np.round(A, decimals=decimal_accuracy)
+        if not A[A == -0].size == 0:
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: Vector contains negative values for zero...
+            Converting to positive values of zero using  ```A[A==-0] = 0```.\n"""))
+        A[A == -0] = 0.00
+        return checkanswer.basic(A, hashtag)
+
+    def make_matrix(A, decimal_accuracy = 5):
+        if(type(A) is not np.matrix):
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: passed variable is {type(A)} and not a numpy.matrix...
+                Trying to convert to a array matrix using ```A = np.matrix(A)```.\n"""))
+            A = np.matrix(A)
+        if not np.issubdtype(A.dtype, np.dtype(float).type):
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: passed matrix is {A.dtype} and not {np.dtype(float).type}...
+                Trying to convert to float using ```A = A.astype(float)```.\n"""))
+            A = A.astype(float)
+        A = np.round(A, decimals=decimal_accuracy)
+        if not A[A == -0].size == 0:
+            printwarning(textwrap.dedent(f"""
+            CheckWarning: Matrix contains negative values for zero...
+                Converting to positive values of zero using  ```A[A==-0] = 0```.\n"""))
+            A[A == -0] = 0.00
+        return A
+    
+    def matrix(A, hashtag=None, decimal_accuracy = 5):
+        """Function to check matrix type before hashing."""
+        A = checkanswer.make_matrix(A, decimal_accuracy)
+        return checkanswer.basic(A, hashtag)
+
+    # TODO: Not complete or tested.
+    def eq_matrix(A, hashtag=None, decimal_accuracy = 5):
+        """Function to convert matrix to reduced row echelon form
+        and then run hashing."""
+        A = checkanswer.make_matrix(A, decimal_accuracy)
+        symA = sym.Matrix(A)
+        symA = symA.rref()[0]
+        A = np.matrix(symA)
+        A = checkanswer.make_matrix(A)
+        return checkanswer.basic(A, hashtag)