From d7fab6ceb9a22b6602d704687e4539641bad6b73 Mon Sep 17 00:00:00 2001 From: Allen Downey Date: Wed, 31 Aug 2016 16:44:57 -0400 Subject: [PATCH] Minor fixes in workshop notebooks --- thinkbayes2.py | 144 ++++++-- workshop01.ipynb | 71 +++- workshop01_soln.ipynb | 747 ++++++++++++++++++++++++++++++++++++------ 3 files changed, 815 insertions(+), 147 deletions(-) diff --git a/thinkbayes2.py b/thinkbayes2.py index 574c53d..853a529 100644 --- a/thinkbayes2.py +++ b/thinkbayes2.py @@ -445,7 +445,7 @@ def Percentile(self, percentage): returns: value from the Pmf """ - p = percentage / 100.0 + p = percentage / 100 total = 0 for val, prob in sorted(self.Items()): total += prob @@ -528,8 +528,6 @@ def Normalize(self, fraction=1): total = self.Total() if total == 0: raise ValueError('Normalize: total probability is zero.') - #logging.warning('Normalize: total probability is zero.') - #return total factor = fraction / total for x in self.d: @@ -547,7 +545,7 @@ def Random(self): float value from the Pmf """ target = random.random() - total = 0.0 + total = 0 for x, p in self.d.items(): total += p if total >= target: @@ -562,10 +560,7 @@ def Mean(self): Returns: float mean """ - mean = 0.0 - for x, p in self.d.items(): - mean += p * x - return mean + return sum(p * x for x, p in self.Items()) def Var(self, mu=None): """Computes the variance of a PMF. @@ -578,10 +573,15 @@ def Var(self, mu=None): if mu is None: mu = self.Mean() - var = 0.0 - for x, p in self.d.items(): - var += p * (x - mu) ** 2 - return var + return sum(p * (x-mu)**2 for x, p in self.Items()) + + def Expect(self, func): + """Computes the expectation of func(x). + + Returns: + expectation + """ + return np.sum(p * func(x) for x, p in self.Items()) def Std(self, mu=None): """Computes the standard deviation of a PMF. @@ -594,7 +594,7 @@ def Std(self, mu=None): var = self.Var(mu) return math.sqrt(var) - def MaximumLikelihood(self): + def MAP(self): """Returns the value with the highest probability. Returns: float probability @@ -602,6 +602,14 @@ def MaximumLikelihood(self): _, val = max((prob, val) for val, prob in self.Items()) return val + # Calling this function MaximumLikelihood is potentially misleading, + # since the highest posterior probability does not necessarily + # correspond to the highest likelihood. MAP, for maximum aposteori + # probability, is better, but still potentially misleading because + # we might apply it to a Pmf that is not a posterior distribution. + # So I'm providing both names. + MaximumLikelihood = MAP + def CredibleInterval(self, percentage=90): """Computes the central credible interval. @@ -628,6 +636,8 @@ def __add__(self, other): except AttributeError: return self.AddConstant(other) + __radd__ = __add__ + def AddPmf(self, other): """Computes the Pmf of the sum of values drawn from self and other. @@ -648,6 +658,9 @@ def AddConstant(self, other): returns: new Pmf """ + if other == 0: + return self.Copy() + pmf = Pmf() for v1, p1 in self.Items(): pmf.Set(v1 + other, p1) @@ -810,7 +823,7 @@ def MaxLikeInterval(self, percentage=90): for prob, val in t: interval.append(val) total += prob - if total >= percentage / 100.0: + if total >= percentage / 100: break return interval @@ -946,7 +959,7 @@ def MakeUniformPmf(low, high, n): return pmf -class Cdf(object): +class Cdf: """Represents a cumulative distribution function. Attributes: @@ -1037,6 +1050,11 @@ def __delitem__(self): def __eq__(self, other): return np.all(self.xs == other.xs) and np.all(self.ps == other.ps) + def Print(self): + """Prints the values and freqs/probs in ascending order.""" + for val, prob in zip(self.xs, self.ps): + print(val, prob) + def Copy(self, label=None): """Returns a copy of this Cdf. @@ -1052,11 +1070,6 @@ def MakePmf(self, label=None): label = self.label return Pmf(self, label=label) - def Values(self): - """Returns a sorted list of values. - """ - return self.xs - def Items(self): """Returns a sorted sequence of (value, probability) pairs. @@ -1097,7 +1110,7 @@ def Prob(self, x): float probability """ if x < self.xs[0]: - return 0.0 + return 0 index = bisect.bisect(self.xs, x) p = self.ps[index-1] return p @@ -1112,7 +1125,7 @@ def Probs(self, xs): xs = np.asarray(xs) index = np.searchsorted(self.xs, xs, side='right') ps = self.ps[index-1] - ps[xs < self.xs[0]] = 0.0 + ps[xs < self.xs[0]] = 0 return ps ProbArray = Probs @@ -1132,15 +1145,20 @@ def Value(self, p): index = bisect.bisect_left(self.ps, p) return self.xs[index] - def ValueArray(self, ps): + def Values(self, ps=None): """Returns InverseCDF(p), the value that corresponds to probability p. + If ps is not provided, returns all values. + Args: ps: NumPy array of numbers in the range [0, 1] Returns: NumPy array of values """ + if ps is None: + return self.xs + ps = np.asarray(ps) if np.any(ps < 0) or np.any(ps > 1): raise ValueError('Probability p must be in range [0, 1]') @@ -1148,6 +1166,8 @@ def ValueArray(self, ps): index = np.searchsorted(self.ps, ps, side='left') return self.xs[index] + ValueArray = Values + def Percentile(self, p): """Returns the value that corresponds to percentile p. @@ -1157,7 +1177,19 @@ def Percentile(self, p): Returns: number value """ - return self.Value(p / 100.0) + return self.Value(p / 100) + + def Percentiles(self, ps): + """Returns the value that corresponds to percentiles ps. + + Args: + ps: numbers in the range [0, 100] + + Returns: + array of values + """ + ps = np.asarray(ps) + return self.Values(ps / 100) def PercentileRank(self, x): """Returns the percentile rank of the value x. @@ -1166,7 +1198,16 @@ def PercentileRank(self, x): returns: percentile rank in the range 0 to 100 """ - return self.Prob(x) * 100.0 + return self.Prob(x) * 100 + + def PercentileRanks(self, xs): + """Returns the percentile ranks of the values in xs. + + xs: potential value in the CDF + + returns: array of percentile ranks in the range 0 to 100 + """ + return self.Probs(x) * 100 def Random(self): """Chooses a random value from this distribution.""" @@ -1188,7 +1229,7 @@ def Mean(self): float mean """ old_p = 0 - total = 0.0 + total = 0 for x, new_p in zip(self.xs, self.ps): p = new_p - old_p total += p * x @@ -1206,13 +1247,13 @@ def CredibleInterval(self, percentage=90): Returns: sequence of two floats, low and high """ - prob = (1 - percentage / 100.0) / 2 + prob = (1 - percentage / 100) / 2 interval = self.Value(prob), self.Value(1 - prob) return interval ConfidenceInterval = CredibleInterval - def _Round(self, multiplier=1000.0): + def _Round(self, multiplier=1000): """ An entry is added to the cdf only if the percentile differs from the previous value in a significant digit, where the number @@ -1671,7 +1712,7 @@ def CredibleInterval(pmf, percentage=90): sequence of two floats, low and high """ cdf = pmf.MakeCdf() - prob = (1 - percentage / 100.0) / 2 + prob = (1 - percentage / 100) / 2 interval = cdf.Value(prob), cdf.Value(1 - prob) return interval @@ -1686,7 +1727,7 @@ def PmfProbLess(pmf1, pmf2): Returns: float probability """ - total = 0.0 + total = 0 for v1, p1 in pmf1.Items(): for v2, p2 in pmf2.Items(): if v1 < v2: @@ -1704,7 +1745,7 @@ def PmfProbGreater(pmf1, pmf2): Returns: float probability """ - total = 0.0 + total = 0 for v1, p1 in pmf1.Items(): for v2, p2 in pmf2.Items(): if v1 > v2: @@ -1722,7 +1763,7 @@ def PmfProbEqual(pmf1, pmf2): Returns: float probability """ - total = 0.0 + total = 0 for v1, p1 in pmf1.Items(): for v2, p2 in pmf2.Items(): if v1 == v2: @@ -1902,6 +1943,32 @@ def MakeExponentialPmf(lam, high, n=200): return pmf +def EvalParetoPdf(x, xm, alpha): + """Computes the Pareto. + + xm: minimum value (scale parameter) + alpha: shape parameter + + returns: float probability density + """ + return stats.pareto.pdf(x, alpha, scale=xm) + + +def MakeParetoPmf(xm, alpha, high, num=101): + """Makes a PMF discrete approx to a Pareto distribution. + + xm: minimum value (scale parameter) + alpha: shape parameter + high: upper bound value + num: number of values + + returns: normalized Pmf + """ + xs = np.linspace(xm, high, num) + ps = stats.pareto.pdf(xs, alpha, scale=xm) + pmf = Pmf(dict(zip(xs, ps))) + return pmf + def StandardNormalCdf(x): """Evaluates the CDF of the standard Normal distribution. @@ -2014,7 +2081,7 @@ def RenderParetoCdf(xmin, alpha, low, high, n=50): return xs, ps -class Beta(object): +class Beta: """Represents a Beta distribution. See http://en.wikipedia.org/wiki/Beta_distribution @@ -2038,6 +2105,12 @@ def Mean(self): """Computes the mean of this distribution.""" return self.alpha / (self.alpha + self.beta) + def MAP(self): + """Computes the value with maximum a posteori probability.""" + a = self.alpha - 1 + b = self.beta - 1 + return a / (a + b) + def Random(self): """Generates a random variate from this distribution.""" return random.betavariate(self.alpha, self.beta) @@ -2071,6 +2144,9 @@ def MakePmf(self, steps=101, label=None): model of the continuous distribution, and behaves well as the number of values increases. """ + if label is None and self.label is not None: + label = self.label + if self.alpha < 1 or self.beta < 1: cdf = self.MakeCdf() pmf = cdf.MakePmf() @@ -2217,7 +2293,7 @@ def LogBinomialCoef(n, k): return n * math.log(n) - k * math.log(k) - (n - k) * math.log(n - k) -def NormalProbability(ys, jitter=0.0): +def NormalProbability(ys, jitter=0): """Generates data for a normal probability plot. ys: sequence of values diff --git a/workshop01.ipynb b/workshop01.ipynb index 20b49cd..b869662 100644 --- a/workshop01.ipynb +++ b/workshop01.ipynb @@ -24,17 +24,17 @@ "source": [ "from __future__ import print_function, division\n", "\n", + "%matplotlib inline\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", "import math\n", "import numpy as np\n", "from scipy.special import gamma\n", "\n", "from thinkbayes2 import Pmf, Suite\n", - "import thinkplot\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "%matplotlib inline" + "import thinkplot" ] }, { @@ -376,9 +376,18 @@ "\n", "Hint: What should you do if the outcome exceeds the hypothetical number of sides on the die?\n", "\n", - "Here's an outline to get you started:\n", - "\n", - " class Dice(Suite):\n", + "Here's an outline to get you started:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Dice(Suite):\n", " # hypo is the number of sides on the die\n", " # data is the outcome\n", " def Likelihood(self, data, hypo):\n", @@ -530,7 +539,25 @@ "\n", "**Exercise 8:** Write a class definition for `Euro`, which extends `Suite` and defines a likelihood function that computes the probability of the data (heads or tails) for a given value of `x` (the probability of heads).\n", "\n", - "Note that `hypo` is in the range 0 to 100." + "Note that `hypo` is in the range 0 to 100. Here's an outline to get you started." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Euro(Suite):\n", + " \n", + " def Likelihood(self, data, hypo):\n", + " \"\"\" \n", + " hypo is the prob of heads (0-100)\n", + " data is a string, either 'H' or 'T'\n", + " \"\"\"\n", + " return 1" ] }, { @@ -813,7 +840,29 @@ "source": [ "**Exercise 9:** Write a class called `Soccer` that extends `Suite` and defines `Likelihood`, which should compute the probability of the data (the time between goals in minutes) for a hypothetical goal-scoring rate, `lam`, in goals per game.\n", "\n", - "Hint: For a given value of `lam`, the time between goals is distributed exponentially." + "Hint: For a given value of `lam`, the time between goals is distributed exponentially.\n", + "\n", + "Here's an outline to get you started:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Soccer(Suite):\n", + " \"\"\"Represents hypotheses about goal-scoring rates.\"\"\"\n", + "\n", + " def Likelihood(self, data, hypo):\n", + " \"\"\"Computes the likelihood of the data under the hypothesis.\n", + "\n", + " hypo: scoring rate in goals per game\n", + " data: interarrival time in minutes\n", + " \"\"\"\n", + " return 1" ] }, { diff --git a/workshop01_soln.ipynb b/workshop01_soln.ipynb index e013246..f380e41 100644 --- a/workshop01_soln.ipynb +++ b/workshop01_soln.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -24,17 +24,17 @@ "source": [ "from __future__ import print_function, division\n", "\n", + "%matplotlib inline\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", "import math\n", "import numpy as np\n", "from scipy.special import gamma\n", "\n", "from thinkbayes2 import Pmf, Suite\n", - "import thinkplot\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "%matplotlib inline" + "import thinkplot" ] }, { @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -85,11 +85,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1\n", + "2 1\n", + "3 1\n", + "4 1\n", + "5 1\n", + "6 1\n" + ] + } + ], "source": [ "d6.Print()" ] @@ -103,11 +116,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "d6.Normalize()" ] @@ -121,11 +145,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 0.166666666667\n", + "2 0.166666666667\n", + "3 0.166666666667\n", + "4 0.166666666667\n", + "5 0.166666666667\n", + "6 0.166666666667\n" + ] + } + ], "source": [ "d6.Print()" ] @@ -139,11 +176,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.5" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "d6.Mean()" ] @@ -157,11 +205,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "d6.Random()" ] @@ -175,11 +234,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Solution\n", "thinkplot.Hist(d6+d6)" @@ -216,11 +297,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "7.393939393939394" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Gf34/3+uQczjO/KOKDpd3vDlLkhrS9+UdSdIyMvqS1BCjL0kNMfqS1BCjL0kNMfqS1BCj\nL0kNMfqS1JD/BRvbyHla71dWAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Solution\n", "\n", @@ -243,11 +345,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bowl 1 0.5\n", + "Bowl 2 0.5\n" + ] + } + ], "source": [ "cookie = Pmf(['Bowl 1', 'Bowl 2'])\n", "cookie.Print()" @@ -262,11 +373,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.625" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cookie['Bowl 1'] *= 0.75\n", "cookie['Bowl 2'] *= 0.5\n", @@ -282,11 +404,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bowl 1 0.6\n", + "Bowl 2 0.4\n" + ] + } + ], "source": [ "cookie.Print()" ] @@ -302,11 +433,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bowl 1 0.428571428571\n", + "Bowl 2 0.571428571429\n" + ] + } + ], "source": [ "# Solution\n", "\n", @@ -329,11 +469,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bowl 1 0.428571428571\n", + "Bowl 2 0.571428571429\n" + ] + } + ], "source": [ "# Solution\n", "\n", @@ -355,11 +504,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 0.25\n", + "6 0.25\n", + "8 0.25\n", + "12 0.25\n" + ] + } + ], "source": [ "suite = Suite([4, 6, 8, 12])\n", "suite.Print()" @@ -376,11 +536,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 0.0\n", + "6 0.444444444444\n", + "8 0.333333333333\n", + "12 0.222222222222\n" + ] + } + ], "source": [ "# Solution\n", "\n", @@ -403,9 +574,18 @@ "\n", "Hint: What should you do if the outcome exceeds the hypothetical number of sides on the die?\n", "\n", - "Here's an outline to get you started:\n", - "\n", - " class Dice(Suite):\n", + "Here's an outline to get you started:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Dice(Suite):\n", " # hypo is the number of sides on the die\n", " # data is the outcome\n", " def Likelihood(self, data, hypo):\n", @@ -414,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -441,11 +621,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 0.0\n", + "6 0.444444444444\n", + "8 0.333333333333\n", + "12 0.222222222222\n" + ] + } + ], "source": [ "dice = Dice([4, 6, 8, 12])\n", "dice.Update(6)\n", @@ -461,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -480,11 +671,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 0.0\n", + "6 0.0\n", + "8 0.919293820933\n", + "12 0.0807061790668\n" + ] + } + ], "source": [ "dice.Print()" ] @@ -500,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -525,11 +727,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "62.822944785168964" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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M6NK+OX1z2tK7extyOrWoFbdEDX8OJaXFL9mdoQ5pqSUa1K/DwD6dGNinE+7O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\nRKSc/w8yJrOJbfgUyQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tank = Tank(range(100))\n", "tank.Update(37)\n", @@ -548,11 +771,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "57.81472076260485" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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T/EZZnzp1ir1791JRUYG6nDz6rZXMz53sPdYChIk0I/eTaEYF3ym7Y0ZgtVJP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\nkcKNVyxgRUEeGanJYcjh0Oj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EsoAT4crfMLoSuF1EDgO/Ba4XkV8Bx6PwXhwFjqjqds/2\ny7iDRTT+XdwAHFbV06rqBP4TuILovBfderv2amCaT7qgvk8jNTh4B96JSDzuwXObwpyn4fYLoERV\nf+Kzr3vgIPQxcHA0UdXvq2qOqubi/jt4R1W/BrxK9N2LWuCIiMz17FoBFBOFfxe4q5MKRCTR07i6\nAneHhWi6F4J/abq3a98E3O3pzTUTmA1s7ffkkTrOQURW4u6Z0T147odhztKwEZErgfeBvZwbCv99\n3P+hL+H+FVAFfFlV68OVz+EmItcC/0VVbxeRcUThvRCRxbgb5uOAw7gHnMYQnfdiHe4fDJ3Ap8C3\ngLFEwb0Qkd8AhcB4oBZYB/wR+B09XLuIfA/4Ju579aCqbu73PSI1OBhjjAmfSK1WMsYYE0YWHIwx\nxpzHgoMxxpjzWHAwxhhzHgsOxhhjzmPBwRhjzHksOBhjjDmPBQdjjDHn+b/PKsl6NiO7sQAAAABJ\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Solution\n", "\n", @@ -571,12 +815,30 @@ "\n", "**Exercise 8:** Write a class definition for `Euro`, which extends `Suite` and defines a likelihood function that computes the probability of the data (heads or tails) for a given value of `x` (the probability of heads).\n", "\n", - "Note that `hypo` is in the range 0 to 100." + "Note that `hypo` is in the range 0 to 100. Here's an outline to get you started." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Euro(Suite):\n", + " \n", + " def Likelihood(self, data, hypo):\n", + " \"\"\" \n", + " hypo is the prob of heads (0-100)\n", + " data is a string, either 'H' or 'T'\n", + " \"\"\"\n", + " return 1" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -607,11 +869,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "euro = Euro(range(101))\n", "thinkplot.Pdf(euro)" @@ -626,11 +899,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "euro.Update('H')\n", "thinkplot.Pdf(euro)" @@ -645,11 +929,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "euro.Update('H')\n", "thinkplot.Pdf(euro)" @@ -664,11 +959,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "euro.Update('T')\n", "thinkplot.Pdf(euro)" @@ -683,11 +989,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "70" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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FhDFDATDAweMF9gbk5zQ5KOWDGhub2Lyr+ZLSCm01eGT25OZeS/u011KXaHJQygcdOH6e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\n8rqrgNec268B999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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "euro = Euro(range(101))\n", "\n", @@ -733,11 +1071,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "55.952380952380956" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "euro.Mean()" ] @@ -751,11 +1100,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "56" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "euro.MaximumLikelihood()" ] @@ -769,11 +1129,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(51, 61)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "euro.CredibleInterval(90)" ] @@ -791,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": { "collapsed": true }, @@ -817,7 +1188,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": { "collapsed": true }, @@ -848,11 +1219,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.3103599490022571" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "xs = np.linspace(0, 8, 101)\n", "pmf = MakeGammaPmf(xs, 1.3)\n", @@ -867,12 +1259,34 @@ "source": [ "**Exercise 9:** Write a class called `Soccer` that extends `Suite` and defines `Likelihood`, which should compute the probability of the data (the time between goals in minutes) for a hypothetical goal-scoring rate, `lam`, in goals per game.\n", "\n", - "Hint: For a given value of `lam`, the time between goals is distributed exponentially." + "Hint: For a given value of `lam`, the time between goals is distributed exponentially.\n", + "\n", + "Here's an outline to get you started:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Soccer(Suite):\n", + " \"\"\"Represents hypotheses about goal-scoring rates.\"\"\"\n", + "\n", + " def Likelihood(self, data, hypo):\n", + " \"\"\"Computes the likelihood of the data under the hypothesis.\n", + "\n", + " hypo: scoring rate in goals per game\n", + " data: interarrival time in minutes\n", + " \"\"\"\n", + " return 1" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": { "collapsed": true }, @@ -904,11 +1318,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.3103599490022564" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "soccer = Soccer(pmf)\n", "thinkplot.Pdf(soccer)\n", @@ -925,11 +1360,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2.0352677560937336" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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BXqkYEreBfnT7/EzLb3M4EhMTQ3LhNDU1TWk642Aiwmc/9gHWnjyyLs3fXniP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\n0ncEm1ucw9mnLOGMExZpSg8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4OEhdXZ2t9xcRPnfdBURHe3+Fj1af5M8v77F1DkWZD0SkCIRqJ/DOoWERWKci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\n9+9d440JyyOiiqIoyswQruYgRVEUZQZQEVAURZn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "thinkplot.Pdf(soccer, color='0.7')\n", "soccer.Update(12)\n", @@ -975,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "collapsed": true }, @@ -1001,7 +1478,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": { "collapsed": true }, @@ -1032,11 +1509,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "lam = soccer.Mean()\n", "rem_time = 90 - 23\n", @@ -1057,11 +1545,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.9377241975748247" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pred.Mean()" ] @@ -1075,11 +1574,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.047208117119541926" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pred.ProbGreater(4)" ] @@ -1097,11 +1607,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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N4oYbbsAtt9yi8+kIgiDsh6UQd6UUyuUyMpkMwjBEv9/X8ek80Nnr9eD7Ptrt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\n0xXMmpA0y6UV1Yvh9/nHbn9+lthHHcO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "for lam, prob in soccer.Items():\n", " lt = lam * rem_time / 90\n", @@ -1120,7 +1641,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": { "collapsed": false }, @@ -1143,7 +1664,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": { "collapsed": true }, @@ -1174,7 +1695,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": { "collapsed": false }, @@ -1192,11 +1713,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "thinkplot.Hist(mix)\n", "thinkplot.Config(title='Option 2', \n", @@ -1213,11 +1745,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(1.9377505061485503, 0.085650136218385256)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Solution\n", "\n",