mlessentials/Lab08/Examples/distributions.ipynb

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    "import matplotlib.pyplot as plt"
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   "source": [
    "# list of all xs\n",
    "x = list(range(1, 11))"
   ]
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   "source": [
    "print(x)"
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   "source": [
    "# pmf\n",
    "p_X_x = [1/len(x)] * len(x)"
   ]
  },
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   "source": [
    "# sums to \n",
    "print(p_X_x)"
   ]
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   "source": [
    "plt.bar(x, p_X_x)\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('P(X=x)')"
   ]
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   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats"
   ]
  },
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   "source": [
    "# range of xs\n",
    "x = np.linspace(-10, 10, 100)"
   ]
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   "source": [
    "# first normal distribution with mean = 0, variance = 1\n",
    "p_X_1 = stats.norm.pdf(x=x, loc=0.0, scale=1.0**2)\n",
    "\n",
    "# second normal distribution with mean = 0, variance = 2.25\n",
    "p_X_2 = stats.norm.pdf(x=x, loc=0.0, scale=1.5**2)"
   ]
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    "plt.plot(x,p_X_1, color='blue')\n",
    "plt.plot(x, p_X_2, color='orange')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('P(X)')"
   ]
  }
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