feat: math chapter done + editorconfig + remove pycache

src/modules/math.py

@@ -1,8 +1,14 @@
+## This module represents the first chapter of the book 
+##  "Essential Math for Data Science" - Thomas Nield
+##  Chaper 1 - Basic Math and Calculus Review 
+
 from cmath import log as complex_log  # used for complex numbers
 from math import e, exp, log
 
+from numpy import arange
+
 # Sympy is powerful since it does not make aproximations, it keeps every primitive as it is, this means that it's slower but more precisse
-from sympy import diff, limit, oo, symbols
+from sympy import diff, limit, oo, symbols, integrate
 
 
 def t_summ():
@@ -71,3 +77,59 @@ def t_build_derivate(f):
 
 def t_build_partial_derivate(f,symbol):
 	return diff(f, symbol)
+
+def t_approximate_integral(f, init, end, precission):
+	integral = 0
+
+	for i in arange(init, end, precission):
+		w = i + precission
+		h = f(w) # or f(i) to estimate down
+		integral += (h * precission)
+	return integral
+
+def t_calculate_integral(f, init, end, symbol):
+    return integrate(f, (symbol, init, end))
+
+def test_math_module():
+	t_exponent(2,8)
+	print(t_compound_interest(100, 20 / 100, 2, 12))
+	print(t_compound_interest_algorigthm(100, 20 / 100, 2, 12))
+	print(t_compound_interest_continious(100, 20 / 100, 2))
+	print(t_compound_interest_continious_exp(100, 20 / 100, 2))
+	print(t_calculate_e(1000000000000))
+	print(t_logarithm_natural(10))
+	print(t_limit())
+	print(t_calculate_e_limit())
+	print(t_calculate_e_limit().evalf())
+	x = symbols("x")
+	derivative = t_build_derivate(x ** 2)
+	value_on_two = derivative.subs(x,2)
+	print(derivative, value_on_two)
+	# Partial derivates are applied to one variable at a time
+	y = symbols("y")
+	z = symbols("z")
+	partial_derivative_y = t_build_partial_derivate((3 * y ** 3) + 2 * z ** 2, y)
+	partial_derivative_z = t_build_partial_derivate((3 * y ** 3) + 2 * z ** 2, z)
+	print(partial_derivative_y, partial_derivative_z)
+	print(t_build_derivative_from_limit(x ** 2))
+	print(t_calculate_derivative_limit(x ** 2, 2))
+
+	## Chain rule
+	# for a given function y (lets say y = x ** 2 + 1) and another z (lets say z = y ** 3 - 2), we can find a derivativeof z with respect to x by multiplying the derivatives
+	# normal derivative calc
+	x = symbols("x")
+	z = (x ** 2 + 1) ** 3 - 2
+	print(t_build_derivate(z))
+	# chain rule calc
+	x, y, z = symbols("x y z")
+	y_f = x ** 2 + 1
+	z_f = y ** 3 - 2
+	print(t_build_derivate(y_f), t_build_derivate(z_f))
+
+	## Integrals
+	def foo(x):
+		return x ** 2 + 1
+	print(t_approximate_integral(foo, 0, 1, 0.001))
+	x = symbols("x")
+	f = x ** 2 + 1
+	print(t_calculate_integral(f, 0, 1, x))

src/modules/probability.py

@@ -0,0 +1,10 @@
+## This module represents the second chapter of the book 
+##  "Essential Math for Data Science" - Thomas Nield
+##  Chaper 2 - Probability 
+
+# 2.0 odds means than an events has twice the probabilities to happen than not
+def p_odds_to_probability(o):
+	return  (o / (1 + o))
+
+def p_probability_to_odds(p):
+	return (p / (1 - p))