hw4

grader/week11.ipynb

@@ -0,0 +1,241 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [10_NumPy_11] 11_NumPy_★_Indexing_and_Slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# A is a 2-d array\n",
+    "def get_column_from_bottom_to_top( A, c ):\n",
+    "  return A[::-1,c]\n",
+    "\n",
+    "def get_odd_rows( A ):\n",
+    "  return A[1::2,:]\n",
+    "\n",
+    "def get_even_column_last_row( A ):\n",
+    "  return A[-1,::2]\n",
+    "\n",
+    "def get_diagonal1( A ):# A is a square matrix\n",
+    "  # from top-left corner down to bottom-right corner\n",
+    "  return np.diag(A)\n",
+    "\n",
+    "def get_diagonal2( A ): # A is a square matrix\n",
+    "  # from top-right corner down to bottom-left corner\n",
+    "  return np.diag(np.fliplr(A))\n",
+    "\n",
+    "exec(input().strip()) #must have this line when submitting to grader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [10_NumPy_12] 11_NumPy_★_Scalar_and_Array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "he\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def toCelsius(f):\n",
+    "  return (f-32)*5/9\n",
+    "\n",
+    "def BMI(wh):\n",
+    "  return wh[:,0]/(wh[:,1]/100)**2 \n",
+    "\n",
+    "def distanceTo(p, Points):\n",
+    "  return np.sqrt((p[0]-Points[:,0])**2+(p[1]-Points[:,1])**2)\n",
+    "\n",
+    "exec(input().strip())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [10_NumPy_13] 11_NumPy_★_Logistic_Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def p(x):\n",
+    "  return 1 / (1 + np.e**(-(-3.98 + 0.1 * x[:,0] + 0.5 * x[:,1])))\n",
+    "\n",
+    "exec(input().strip())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [10_NumPy_21] 11_NumPy_★★_Slicing and Element-wise Op."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def sum_2_rows(M):\n",
+    "  return M[::2,:]+M[1::2,:]\n",
+    "\n",
+    "def sum_left_right(M):\n",
+    "  return M[:,:M.shape[1]//2]+M[:,M.shape[1]//2:]\n",
+    "\n",
+    "def sum_upper_lower(M):\n",
+    "  return M[:M.shape[0]//2,:]+M[M.shape[0]//2:,:]\n",
+    "\n",
+    "def sum_4_quadrants(M):\n",
+    "  return sum_left_right(sum_upper_lower(M))\n",
+    "\n",
+    "def sum_4_cells(M):\n",
+    "  return M[::2,::2]+M[::2,1::2]+M[1::2,::2]+M[1::2,1::2]\n",
+    "\n",
+    "def count_leap_years(years):\n",
+    "  return np.sum((years%4==0)&((years%100!=0)|(years%400==0)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [10_NumPy_22] 11_NumPy_★★_Outer_Product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def mult_table(nrows, ncols):\n",
+    "  return np.outer(np.arange(1,nrows+1),np.arange(1,ncols+1))\n",
+    "\n",
+    "exec(input().strip())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [10_NumPy_23] 11_NumPy_★★_Lower_than_Mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wtf\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "read_data=lambda:(np.array([float(x)for x in input().split()]),np.array([[int(r)for r in input().split()]for i in'0'*int(input())]))\n",
+    "\n",
+    "def report_lower_than_mean(weight, data):\n",
+    "  mean = np.mean(data[:,1:] * weight)\n",
+    "  result = data[np.mean(weight * data[:,1:], axis=1) < mean][:,0]\n",
+    "  print(\", \".join([str(id) for id in result]) if result.shape[0] > 0 else \"None\")\n",
+    "\n",
+    "exec(input().strip())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [10_NumPy_24] 11_NumPy_★★_Peak_Indexes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def peak_indexes(x):\n",
+    "  # x is an array containing values\n",
+    "  # return an array listing indexes of “peaks”\n",
+    "  pass\n",
+    "\n",
+    "def main():\n",
+    "  d = np.array([float(e) for e in input().split()])\n",
+    "  pos = peak_indexes(np.array(d))\n",
+    "  if len(pos) > 0:\n",
+    "    print(\", \".join([str(e) for e in pos]))\n",
+    "  else:\n",
+    "    print(\"No peaks\")\n",
+    "\n",
+    "exec(input().strip()) # Don't remove this line\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.10 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}