cleared notebooks 00 and 02

02-Dynamic-Programming.ipynb

@@ -23,18 +23,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "55\n",
-      "54.9 µs ± 10.4 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def naive_fib(n):\n",
     "    if n == 0:\n",
@@ -57,18 +48,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "55\n",
-      "4.13 µs ± 285 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def dp_fib(n):\n",
     "    if n == 0:\n",
@@ -100,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -124,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -146,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -167,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -188,30 +170,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0.6281491025812429)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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YaJak87LBTzNdDkNB0ujb4KeZLoehIGn0bfDTTJfDUJA0+jb4aabL0drZR5K0rkxNGQJ9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\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1e64afc2eb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "from matplotlib import pyplot as plt\n",
 %% Cell type:markdown id: tags:
 
 # CO202 - Algorithms 2
 
 %% Cell type:markdown id: tags:
 
 ## Tutorial on Dynamic Programming: Fibonacci
 
 %% Cell type:markdown id: tags:
 
 ### Naive Divide-and-Conquer Approach
 
 %% Cell type:code id: tags:
 
 ``` python
 def naive_fib(n):
     if n == 0:
         return 0
     elif n == 1:
         return 1
     else:
         return naive_fib(n-1) + naive_fib(n-2)
 
 print(naive_fib(10))
 %timeit naive_fib(10)
 ```
 
-%%%% Output: stream
-
-    55
-    54.9 µs ± 10.4 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
-
 %% Cell type:markdown id: tags:
 
 ### Bottom-up Dynamic Programming Approach
 
 %% Cell type:code id: tags:
 
 ``` python
 def dp_fib(n):
     if n == 0:
         return 0
     f = [0]*(n+1)
     f[0] = 0
     f[1] = 1
     for i in range(2,n+1):
         f[i] = f[i-1] + f[i-2]
     return f[n]
 
 print(dp_fib(10))
 %timeit dp_fib(10)
 ```
 
-%%%% Output: stream
-
-    55
-    4.13 µs ± 285 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
-
 %% Cell type:markdown id: tags:
 
 ### Comparison
 
 %% Cell type:markdown id: tags:
 
 #### Timer
 
 %% Cell type:code id: tags:
 
 ``` python
 import time
 
 def time_f(f):
     before = time.clock()
     f()
     after = time.clock()
     return after - before
 ```
 
 %% Cell type:markdown id: tags:
 
 #### Test data
 
 %% Cell type:code id: tags:
 
 ``` python
 #data = range(1,20)
 data = range(1,30)
 #data = range(1,1000,10)
 #data = range(1,10000,100)
 #data = range(1,100000,1000)
 ```
 
 %% Cell type:markdown id: tags:
 
 #### Running Divide-and-Conquer
 
 %% Cell type:code id: tags:
 
 ``` python
 tdc = []
 
 for i in data:
     tdc.append(time_f(lambda: naive_fib(i)))
 ```
 
 %% Cell type:markdown id: tags:
 
 #### Running Dynamic Programming
 
 %% Cell type:code id: tags:
 
 ``` python
 tdp = []
 
 for i in data:
     tdp.append(time_f(lambda: dp_fib(i)))
 ```
 
 %% Cell type:markdown id: tags:
 
 #### Plot DC vs DP
 
 %% Cell type:code id: tags:
 
 ``` python
 %matplotlib inline
 from matplotlib import pyplot as plt
 
 plt.scatter(data, tdc, c='red')
 plt.scatter(data, tdp, c='blue')
 plt.xlabel('n')
 plt.ylabel('time (/s)')
 plt.xlim(0)
 plt.ylim(0)
 ```
 
-%%%% Output: execute_result
-
-    (0, 0.6281491025812429)
-
-%%%% Output: display_data
-
-
-
 %% Cell type:code id: tags:
 
 ``` python
 ```