From 622cca31dff68451f7ad275478433bd7e2040596 Mon Sep 17 00:00:00 2001 From: iqbo Date: Thu, 24 Sep 2020 19:07:04 +0000 Subject: [PATCH 1/3] commiting... --- Practice0.ipynb | 36 +++++++++++++++++++++++++++++++++--- 1 file changed, 33 insertions(+), 3 deletions(-) diff --git a/Practice0.ipynb b/Practice0.ipynb index 4b16c03..34fbc1b 100644 --- a/Practice0.ipynb +++ b/Practice0.ipynb @@ -135,7 +135,25 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#Given a number n, print n-th Fibonacci Number.\n", + "fibdigs = [0,1]\n", + "def fibonacci(n):\n", + " savedLen = len(fibdigs)\n", + " if n <= savedLen:\n", + " return fibdigs[n-1]\n", + " else:\n", + " temp = fibonacci(n-1)+fibonacci(n-2)\n", + " fibdigs.append(temp)\n", + " return temp\n", + "\n", + "for x in range(1000000):\n", + " a = fibonacci(x)\n", + " if a > 1000000:\n", + " break\n", + " print(a)\n", + " " + ] }, { "cell_type": "markdown", @@ -153,7 +171,19 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "sum = 0\n", + "average = 0\n", + "numOfTosses = 1000\n", + "for x in range(numOfTosses):\n", + " heads = np.random.binomial(1, .5, 1)\n", + " #print(heads)\n", + " sum = sum + heads\n", + " #print(\"Sum = {p1}\".format(p1 = sum))\n", + "\n", + "average = sum/numOfTosses\n", + "print(\"Average = {p1}\".format(p1 = average))\n" + ] }, { "cell_type": "markdown", @@ -190,7 +220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.8.2" } }, "nbformat": 4, From 33a327aba47f2a920fd18949a71c4af8b27a7810 Mon Sep 17 00:00:00 2001 From: iqbo Date: Thu, 24 Sep 2020 20:44:54 +0000 Subject: [PATCH 2/3] commit --- Practice0.ipynb | 107 +++++++++++++++++++++++------------------------- 1 file changed, 52 insertions(+), 55 deletions(-) diff --git a/Practice0.ipynb b/Practice0.ipynb index 34fbc1b..8b6e329 100644 --- a/Practice0.ipynb +++ b/Practice0.ipynb @@ -9,7 +9,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "IPython version: %6.6s 6.1.0\n" + "IPython version: %6.6s 7.17.0\n" ] } ], @@ -38,24 +38,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Make a 2 row x 3 column array of random numbers\n", - "[[ 0.20354485 0.87353642 0.79226415]\n", - " [ 0.26457656 0.23486214 0.8240387 ]]\n", - "Add 5 to every element\n", - "[[ 5.20354485 5.87353642 5.79226415]\n", - " [ 5.26457656 5.23486214 5.8240387 ]]\n", - "Get the first row\n", - "[ 5.20354485 5.87353642 5.79226415]\n" - ] - } - ], + "outputs": [], "source": [ "#Here is what numpy can do\\n\",\n", "print (\"Make a 2 row x 3 column array of random numbers\")\n", @@ -74,20 +59,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# IPython is quite modern: just press at the end of the unfinished statement to see the documentation\n", "# on possible completions.\n", @@ -97,20 +71,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "%matplotlib inline \n", "import matplotlib.pyplot as plt\n", @@ -131,9 +94,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "#Given a number n, print n-th Fibonacci Number.\n", @@ -166,11 +127,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average = [0.503]\n" + ] + } + ], "source": [ "sum = 0\n", "average = 0\n", @@ -194,12 +161,42 @@ "### use numpy.random.normal to generate gaussian distribution" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline \n", + "import matplotlib.pyplot as plt\n", + "heads = np.random.binomial(500, .5, size=500)\n", + "histogram = plt.hist(heads, bins=10)" + ] + }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], "source": [] } From d896b041433767343b5f730b4d2fa47e46c4d094 Mon Sep 17 00:00:00 2001 From: iqbo Date: Thu, 24 Sep 2020 22:47:32 +0000 Subject: [PATCH 3/3] commit --- Practice0.ipynb | 116 ++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 103 insertions(+), 13 deletions(-) diff --git a/Practice0.ipynb b/Practice0.ipynb index 8b6e329..367bb65 100644 --- a/Practice0.ipynb +++ b/Practice0.ipynb @@ -38,9 +38,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Make a 2 row x 3 column array of random numbers\n", + "[[0.86991553 0.79218612 0.5805647 ]\n", + " [0.88267453 0.46020371 0.04328596]]\n", + "Add 5 to every element\n", + "[[5.86991553 5.79218612 5.5805647 ]\n", + " [5.88267453 5.46020371 5.04328596]]\n", + "Get the first row\n", + "[5.86991553 5.79218612 5.5805647 ]\n" + ] + } + ], "source": [ "#Here is what numpy can do\\n\",\n", "print (\"Make a 2 row x 3 column array of random numbers\")\n", @@ -59,9 +74,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# IPython is quite modern: just press at the end of the unfinished statement to see the documentation\n", "# on possible completions.\n", @@ -71,9 +97,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Kso9Bnbcn+TkmZwxU1SNV9aOq+jHwVxz5k39ixsDgTPC67s/+LwA/ZnDjqkkaA0lWAL8LXD3UfZLGsBV4dPnvGLfn0igm+o/2xeACxCeAD8xp3wzcA0zNaX8OP3nx5QFGf/HlaGPYOLT8JuDabvlcfvLiyxfG9ecwp88+jlxQnZgxAKuHli9iML87ac+l1wF/3C0/m8EUQCZpDN22zcDn5rRNzBgYzL2/qFveBOzplsfi9TCyf7Cj/CP+BoOLFHcCd3Rf5zC4qPLQUNtHh/a5hMEV9fvorlyP6Rg+BdzVtf89g4usjz5xPtyN4cvA9LiOYU6f4XCfmDEAf9PVeCeD+x0Nh/2kPJdOBv62ez7dDrx40sbQbfs48Lp59pmIMXTtexj8MtoN/ErXfyxeD95+QJIaNHZz7pKk/gx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KD/BdmBfYwgk1d/AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline \n", "import matplotlib.pyplot as plt\n", @@ -93,9 +132,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "0\n", + "1\n", + "1\n", + "2\n", + "3\n", + "5\n", + "8\n", + "13\n", + "21\n", + "34\n", + "55\n", + "89\n", + "144\n", + "233\n", + "377\n", + "610\n", + "987\n", + "1597\n", + "2584\n", + "4181\n", + "6765\n", + "10946\n", + "17711\n", + "28657\n", + "46368\n", + "75025\n", + "121393\n", + "196418\n", + "317811\n", + "514229\n", + "832040\n" + ] + } + ], "source": [ "#Given a number n, print n-th Fibonacci Number.\n", "fibdigs = [0,1]\n", @@ -127,14 +205,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average = [0.503]\n" + "Average = [0.521]\n", + "Standard Deviation = 0.4995588053472784\n" ] } ], @@ -142,14 +221,18 @@ "sum = 0\n", "average = 0\n", "numOfTosses = 1000\n", + "dataList = []\n", "for x in range(numOfTosses):\n", " heads = np.random.binomial(1, .5, 1)\n", + " dataList.append(heads)\n", " #print(heads)\n", " sum = sum + heads\n", " #print(\"Sum = {p1}\".format(p1 = sum))\n", "\n", "average = sum/numOfTosses\n", - "print(\"Average = {p1}\".format(p1 = average))\n" + "stdDev = np.std(dataList)\n", + "print(\"Average = {p1}\".format(p1 = average))\n", + "print(\"Standard Deviation = {p1}\".format(p1 = stdDev))" ] }, { @@ -163,12 +246,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -193,6 +276,13 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null,