GMM API

GMM_API.ipynb

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    "class DensityMixin(object):\n",
    "    def score(self, X):\n",
    "        \"\"\"Return the log liklihood of the model given the data X, #2473, #4062.\"\"\"\n",
    "        return np.sum(self.score_samples(X))\n",
    "    \n",
    "    def density(self, X):\n",
    "        \"\"\"Return the density value of the data\"\"\"\n",
    "        return np.exp(self.score_samples(X))\n",
    "    \n",
    "    \n",
    "class GaussianMixture(DensityMixin, BaseEstimator):\n",
    "    def __init__(self, n_components=1, covariance_type='diag',\n",
    "                 random_state=None, thresh=None, tol=1e-3, min_covar=1e-3,\n",
    "                 n_iter=100, n_init=1, params='wmc', init_params='wmc',\n",
    "                 verbose=0):\n",
    "        \"\"\"\n",
    "        Comment:\n",
    "        1. covariance_type=\"full\" would be the usual case\n",
    "        2. deprecate ``params`` ? #4429\n",
    "        \"\"\"\n",
    "        pass\n",
    "    \n",
    "    def _get_covars(self):\n",
    "        \"\"\"Covariance parameters for each mixture component.\n",
    "        The shape depends on `cvtype`::\n",
    "\n",
    "            (`n_states`, 'n_features')                if 'spherical',\n",
    "            (`n_features`, `n_features`)              if 'tied',\n",
    "            (`n_states`, `n_features`)                if 'diag',\n",
    "            (`n_states`, `n_features`, `n_features`)  if 'full'\n",
    "            \n",
    "        Comment: Change it to be public, #1528\n",
    "        \"\"\"\n",
    "        \n",
    "    \n",
    "    def score_samples(self, X):\n",
    "        \"\"\"Return the per-sample likelihood of the data under the model.\n",
    "\n",
    "        Compute the log probability of X under the model and\n",
    "        return the posterior distribution (responsibilities) of each\n",
    "        mixture component for each element of X.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        X: array_like, shape (n_samples, n_features)\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        logprob : array_like, shape (n_samples,)\n",
    "             Log probabilities of each data point in X.\n",
    "             \n",
    "        Comment: log likelihood given the data or probability of the data?\n",
    "        what should score in score_sampling mean? I think it is intended for each sample, \n",
    "        it should be the probability of the data, refer to 'likelihood function' in wiki.\n",
    "        So should we just keep using the name score_sample?\n",
    "        \n",
    "        Currently it returns a tuple of log_probability, responsibility\n",
    "        it should return only log_probability #2473\n",
    "        \n",
    "        \"\"\"\n",
    "        pass \n",
    "    \n",
    "\n",
    "    def score(self, X, y=None):\n",
    "        \"\"\"Compute the log probability under the model.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array_like, shape (n_samples, n_features)\n",
    "            List of n_features-dimensional data points.  Each row\n",
    "            corresponds to a single data point.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        logprob : array_like, shape (n_samples,)\n",
    "            Log probabilities of each data point in X\n",
    "            \n",
    "        Comment: \n",
    "        1. Move it to the DensityMixin, #2473\n",
    "        2. It should return one single number, the likelihood of the model given the data\n",
    "        \"\"\"\n",
    "        pass\n",
    "        "
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