infinite-Joy/different_theta_vals.ipynb

## different_theta_vals.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sCbpKCYgCXyQZ1a1rZ/ivvmrh/9BDsGWLfRNo3x4mTLBvAZroTSkKfJFk1769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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa408abda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import linspace\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "t = linspace(0, 2*math.pi, 400)\n",
    "\n",
    "def linear_hypotheses(a, b, x):\n",
    "    return a*x + b\n",
    "\n",
    "a = linear_hypotheses(random.randint(-10, 10), random.randint(-10, 10), t)\n",
    "b = linear_hypotheses(random.randint(-10, 10), random.randint(-10, 10), t)\n",
    "c = linear_hypotheses(random.randint(-10, 10), random.randint(-10, 10), t)\n",
    "\n",
    "plt.plot(t, a, 'r') # plotting t, a separately \n",
    "plt.plot(t, b, 'b') # plotting t, b separately \n",
    "plt.plot(t, c, 'g') # plotting t, c separately \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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sCbpKCYgCXyQZ1a1rZ/ivvmrh/9BDsGWLfRNo3x4mTLBvAZroTSkKfJFk1769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\nAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7ffa408abda0>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"from numpy import linspace\n",
	"import math\n",
	"import matplotlib.pyplot as plt\n",
	"import random\n",
	"\n",
	"t = linspace(0, 2*math.pi, 400)\n",
	"\n",
	"def linear_hypotheses(a, b, x):\n",
	" return a*x + b\n",
	"\n",
	"a = linear_hypotheses(random.randint(-10, 10), random.randint(-10, 10), t)\n",
	"b = linear_hypotheses(random.randint(-10, 10), random.randint(-10, 10), t)\n",
	"c = linear_hypotheses(random.randint(-10, 10), random.randint(-10, 10), t)\n",
	"\n",
	"plt.plot(t, a, 'r') # plotting t, a separately \n",
	"plt.plot(t, b, 'b') # plotting t, b separately \n",
	"plt.plot(t, c, 'g') # plotting t, c separately \n",
	"plt.show()"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"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": "3.6.1"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}