assignment submission

7159CEM-Portfolio-main/ACO.ipynb

@@ -349,14 +349,199 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "github test\n",
-    "............................................................\n",
-    "............................................................\n",
-    "............................................................\n",
-    "............................................................\n",
-    "............................................................\n",
-    "............................................................\n",
-    "```"
+    "The Ant Colony optimisation was successfully executed using the Sci-kit package, using the aca.run() function. \n",
+    "\n",
+    "## For n = 50 \n",
+    "\n",
+    "Results are as follows :- \n",
+    "\n",
+    "Cost of travel, Euclidean =  117.30634693659576\n",
+    "Cost of travel, Euclidean - Greedy= 124.5160306576604\n",
+    "Cost of travel for asymmetric graph is =  117\n",
+    "Cost of travel for asymmetric graph Greedy= 71\n",
+    "Cost of travel for symmetric graph is =  87\n",
+    "Cost of travel for symmetric graph Greedy= 81\n",
+    "\n",
+    "## Images of graphs and plots \n",
+    "\n",
+    "\n",
+    "$$ Euclidean \\space graph $$\n",
+    "\n",
+    "![title](ACO/EcuGraphn50.png)\n",
+    "\n",
+    "$$ Euclidean \\space Plot $$\n",
+    " \n",
+    "![title](ACO/Eucpltn50.png)\n",
+    "\n",
+    "\n",
+    "$$ Asymmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/Asygraph50.png)\n",
+    "\n",
+    "$$ Asymmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/Asyplt50.png)\n",
+    "\n",
+    "\n",
+    "$$ Symmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/symgraph50.png)\n",
+    "\n",
+    "$$ Symmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/symplt50.png)\n",
+    "\n",
+    "$$ $$ \n",
+    "\n",
+    "## For n = 100\n",
+    "\n",
+    "Results are as follows :- \n",
+    "\n",
+    "Cost of travel, Euclidean =  178.95818153604972\n",
+    "Cost of travel, Euclidean - Greedy= 206.00052858467922\n",
+    "Cost of travel for asymmetric graph is =  182\n",
+    "Cost of travel for asymmetric graph Greedy= 120\n",
+    "Cost of travel for symmetric graph is =  211\n",
+    "Cost of travel for symmetric graph Greedy= 132\n",
+    "\n",
+    "\n",
+    "$$ Euclidean \\space graph $$\n",
+    "\n",
+    "![title](ACO/EcuGraphn100.png)\n",
+    "\n",
+    "$$ Euclidean \\space Plot $$\n",
+    " \n",
+    "![title](ACO/Eucplt100.png)\n",
+    "\n",
+    "\n",
+    "$$ Asymmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/Asygraph100.png)\n",
+    "\n",
+    "$$ Asymmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/Asyplt100.png)\n",
+    "\n",
+    "\n",
+    "$$ symmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/symgraph100.png)\n",
+    "\n",
+    "$$ symmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/symplt100.png)\n",
+    "\n",
+    "\n",
+    "$$ $$ \n",
+    "\n",
+    "## For n = 150\n",
+    "\n",
+    "Results are as follows :- \n",
+    "\n",
+    "Cost of travel, Euclidean =  178.95818153604972\n",
+    "Cost of travel, Euclidean - Greedy= 206.00052858467922\n",
+    "Cost of travel for asymmetric graph is =  182\n",
+    "Cost of travel for asymmetric graph Greedy= 120\n",
+    "Cost of travel for symmetric graph is =  211\n",
+    "Cost of travel for symmetric graph Greedy= 132\n",
+    "\n",
+    "\n",
+    "$$ Euclidean \\space graph $$\n",
+    "\n",
+    "![title](ACO/Asygraph150.png)\n",
+    "\n",
+    "$$ Euclidean \\space Plot $$\n",
+    " \n",
+    "![title](ACO/Eucplt150.png)\n",
+    "\n",
+    "\n",
+    "$$ Asymmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/ecu(asy)graph150.png)\n",
+    "\n",
+    "$$ Asymmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/asyplot150.png)\n",
+    "\n",
+    "\n",
+    "$$ Symmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/symgraph150.png)\n",
+    "\n",
+    "$$ Symmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/symplot150.png)\n",
+    "\n",
+    "\n",
+    "## For n = 200\n",
+    "Results are as follows :- \n",
+    "Cost of travel, Euclidean =  287.6057902102739\n",
+    "Cost of travel, Euclidean - Greedy= 301.31792232560036\n",
+    "Cost of travel for asymmetric graph is =  242\n",
+    "Cost of travel for asymmetric graph Greedy= 220\n",
+    "Cost of travel for symmetric graph is =  210\n",
+    "Cost of travel for symmetric graph Greedy= 221\n",
+    "\n",
+    "$$ Euclidean \\space graph $$\n",
+    "\n",
+    "![title](ACO/Ecugraph200.png)\n",
+    "\n",
+    "$$ Euclidean \\space Plot $$\n",
+    "\n",
+    "![title](ACO/ecuplt200.png)\n",
+    "\n",
+    "$$ Asymmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/Asygrf200.png)\n",
+    "\n",
+    "$$ Asymmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/Asyplt200.png)\n",
+    "\n",
+    "\n",
+    "$$ Symmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/symgrf200.png)\n",
+    "\n",
+    "$$ Symmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/symplt200.png)\n",
+    "\n",
+    "\n",
+    "## For n = 250\n",
+    "Results are as follows :- \n",
+    "Cost of travel, Euclidean =  280.671643559593\n",
+    "Cost of travel, Euclidean - Greedy= 307.56913052707324\n",
+    "Cost of travel for asymmetric graph is =  221\n",
+    "Cost of travel for asymmetric graph Greedy= 225\n",
+    "Cost of travel for symmetric graph is =  422\n",
+    "Cost of travel for symmetric graph Greedy= 225\n",
+    "\n",
+    "$$ Euclidean \\space graph $$\n",
+    "\n",
+    "![title](ACO/Ecugrf250.png)\n",
+    "\n",
+    "$$ Euclidean \\space Plot $$\n",
+    "\n",
+    "![title](ACO/ecuplt250.png)\n",
+    "\n",
+    "$$ Asymmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/Asygrf250.png)\n",
+    "\n",
+    "$$ Asymmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/Asyplt250.png)\n",
+    "\n",
+    "\n",
+    "$$ Symmetric \\space graph $$\n",
+    "\n",
+    "![title](ACO/symgrf250.png)\n",
+    "\n",
+    "$$ Symmetric \\space Plot $$\n",
+    " \n",
+    "![title](ACO/symplt250.png)\n",
+    "\n"
    ]
   },
   {

7159CEM-Portfolio-main/ACO/Dynamic.txt

@@ -0,0 +1,81 @@
+def top_down(arr, n):
+	memo = {}
+
+	# recursive top down memoized solution
+	def solve(i, j):
+		if i > j or i >= n or j < 0:
+			return 0
+
+		k = (i, j)
+		if k in memo:
+			return memo[k]
+
+		# if the user chooses ith number, the opponent can choose from i+1th or jth number.
+		# if he chooses i+1th number, user is left with [i+2,j] range.
+		# if opp chooses jth number, then user is left with [i+1,j-1] range to choose from.
+		# Also opponent tries to choose
+		# in such a way that the user has minimum value left.
+		option1 = arr[i] + min(solve(i+2, j), solve(i+1, j-1))
+
+		# if user chooses jth number, opponent can choose ith number or j-1th number.
+		# if opp chooses ith number,user can choose in range [i+1,j-1].
+		# if opp chooses j-1th number, user can choose in range [i,j-2].
+		option2 = arr[j] + min(solve(i+1, j-1), solve(i, j-2))
+
+		# since the user wants to get maximum money
+		memo[k] = max(option1, option2)
+		return memo[k]
+
+	return solve(0, n-1)
+
+def bot_up(arr, n):
+
+    #Create a table to store
+    #solutions of subproblems
+    table = [[0 for i in range(n)]
+             for i in range(n)]
+
+    # Fill table using above recursive
+    # formula. Note that the table is
+    # filled in diagonal fashion
+    # from diagonal elements to
+    # table[0][n-1] which is the result.
+    for gap in range(n):
+        for j in range(gap, n):
+            i = j - gap
+
+            # Here x is value of F(i + 2, j),
+            # y is F(i + 1, j-1) and z is
+            # F(i, j-2) in above recursive
+            # formula
+            x = 0
+            if((i + 2) <= j):
+                x = table[i + 2][j]
+            y = 0
+            if((i + 1) <= (j - 1)):
+                y = table[i + 1][j - 1]
+            z = 0
+            if(i <= (j - 2)):
+                z = table[i][j - 2]
+            table[i][j] = max(arr[i] + min(x, y),
+                              arr[j] + min(y, z))
+    return table[0][n - 1]
+
+
+
+# Test Code
+list1 = [5, 3, 7, 10]
+n = len(list1)
+print('Maximum Profit of P1 using Top Down approach = ', top_down(list1, n))
+
+print('Maximum Profit of P1 using Bottom Up approach = ', bot_up(list1, n))
+
+list2 = [8, 15, 3, 7]
+n = len(list2)
+print('Maximum Profit of P1 using Top Down approach = ',top_down(list2, n))
+print('Maximum Profit of P1 using Bottom Up approach = ', bot_up(list2, n))
+
+list3 = [20, 30, 2, 2, 2, 10]
+n = len(list3)
+print( 'Maximum Profit of P1 using Top Down approach = ',top_down(list3, n))
+print('Maximum Profit of P1 using Bottom Up approach = ', bot_up(list3, n))

7159CEM-Portfolio-main/ACO/LP note pad.txt

@@ -0,0 +1,38 @@
+# Import all classes of PuLP module
+from pulp import *
+
+# Create the problem variable to contain the problem data
+model = LpProblem("ToolsProblem", LpMaximize)
+
+# Create 3 variables Steel, Molding_machine, and Assembly_machine
+
+T1 = LpVariable("Tool1", 0, None, LpInteger)
+T2 = LpVariable("Tool2", 0, None, LpInteger) 
+T3 = LpVariable("Tool3", 0, None, LpInteger)
+
+# Create maximize objective function
+model += 0.13 * T1 + 0.1 * T2 + 0.12 * T3 
+
+#Constraints:
+
+model += 1*T1 + 0.7 * T2 + 0.6 * T3 <= 10000, "Steel"
+model += 1*T1 + 1 * T2 + 1 * T3 <= 20000, "Molding_machine"
+model += 0.3*T1 + 0.5 * T2 + 0.4 * T3 <= 9000, "Assembly_machine"
+
+#Demand limits:
+
+model += T1 <= 15000, "T1_DemandLimit"
+model += T2 <= 16000, "T2_DemandLimit"
+model += T3 <= 12000, "T3_DemandLimit"
+
+model.solve()
+
+for v in model.variables():
+    print(v.name, "=", v.varValue)
+
+# print("T1 = ", T1.varValue)
+# print("T2 = ", T2.varValue)
+# print("T3 =" , T3.varValue)
+
+
+

7159CEM-Portfolio-main/ACO/Tree's problem.txt

@@ -0,0 +1,60 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn import tree
+from sklearn.metrics import accuracy_score
+import warnings
+
+warnings.filterwarnings("ignore")
+
+data1 = pd.read_csv('datasets/weather.numeric.csv')
+data2 = pd.read_csv('datasets/weather.nominal.csv')
+
+
+X= data1.drop(columns=['play'], axis=1)
+
+y= data1['play']
+
+X_train, X_test, y_train, y_test= train_test_split(X,y,test_size= 0.3)
+
+d1tree= DecisionTreeClassifier()
+d1tree.fit(X_train,y_train)
+predictions= d1tree.predict(X_test)
+
+tree.plot_tree(d1tree)
+plt.show()
+
+from sklearn.metrics import classification_report, confusion_matrix
+print("Accuracy:",accuracy_score(y_test,predictions))
+
+print(confusion_matrix(y_test,predictions))
+print('\n')
+print(classification_report(y_test,predictions))
+
+from sklearn import preprocessing
+string_to_int= preprocessing.LabelEncoder()    #encode your data
+data2=data2.apply(string_to_int.fit_transform) #fit and transform it
+
+#To divide our data into attribute set and Label:
+feature_cols = ['outlook','temperature','humidity','windy']
+X = data2[feature_cols]                               #contains the attribute 
+y = data2.play                                #contains the label
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30) 
+
+d2tree =DecisionTreeClassifier(criterion="entropy", random_state=100)     # create a classifier object
+d2tree.fit(X_train, y_train) 
+
+y_pred= d2tree.predict(X_test)  
+
+
+print("Accuracy:",accuracy_score(y_test, y_pred))
+
+print(confusion_matrix(y_test, y_pred))  
+print(classification_report(y_test, y_pred)) 
+
+tree.plot_tree(d2tree)
+plt.show()