mvp day 1 cookies, solving, and recipes

eating_cookies/eating_cookies.py

@@ -5,8 +5,13 @@
 # The cache parameter is here for if you want to implement
 # a solution that is more efficient than the naive 
 # recursive solution
-def eating_cookies(n, cache=None):
-  pass
+def eating_cookies(n):
+  if n == 0:
+    return 1
+  elif n < 1:
+    return 0
+  else:
+    return eating_cookies(n-1) + eating_cookies(n-2) + eating_cookies(n-3)
 
 if __name__ == "__main__":
   if len(sys.argv) > 1:

making_change/making_change.py

@@ -2,8 +2,16 @@
 
 import sys
 
+total = 0
+count = 0
+sub_count = 1
+
 def making_change(amount, denominations):
-  pass 
+  test = [1] + [0] * (amount)
+  for index in denominations:
+    for place in range(index, amount +1):
+      test[place] = test[place] + test[place - index]
+  return test[amount]
 
 
 if __name__ == "__main__":

recipe_batches/recipe_batches.py

@@ -3,7 +3,22 @@
 import math
 
 def recipe_batches(recipe, ingredients):
-  pass 
+  batches = -1
+  no_more_ingredients = False
+
+  while no_more_ingredients == False:
+    batches +=1
+
+    for item in recipe:
+      if item in ingredients.keys():
+        if recipe[item] <= ingredients[item]:
+          ingredients[item] -= recipe[item]
+        else:
+          no_more_ingredients = True
+          break
+
+
+    return batches
 
 
 if __name__ == '__main__':

rock_paper_scissors/rps.py

@@ -2,8 +2,29 @@
 
 import sys
 
+def rps_recursion(number, list):
+  if number == 0:
+    return number
+
+
 def rock_paper_scissors(n):
   pass 
+  result_array = []
+  selection = ['rock', 'paper', 'scissors']
+
+  def recursive_listing(iterations, result=[]):
+    if iterations == 0:
+      return result_array.append(result)
+    for choice in selection:
+      recursive_listing(iterations -1, result + [choice])
+
+  recursive_listing(n)
+  return result_array
+
+
+
+
+
 
 
 if __name__ == "__main__":

stock_prices/stock_prices.py

@@ -3,7 +3,19 @@
 import argparse
 
 def find_max_profit(prices):
-  pass
+  max_profit = 0
+  stored_value = prices[0]
+  max_profit = prices[1]-prices[0]
+  index = 2
+  while True:
+    if index > len(prices):
+      break
+    for price in prices[index-1:]:
+      if price - stored_value > max_profit:
+        max_profit = price - stored_value
+    stored_value=prices[index-1]
+    index += 1
+  return max_profit
 
 
 if __name__ == '__main__':