FIX: Changed named to choice, reverted other changes.

2 files changed, 60 insertions, 74 deletions

diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c
index 5726598bb..53713221c 100644
--- a/numpy/random/mtrand/mtrand.c
+++ b/numpy/random/mtrand/mtrand.c
@@ -1,4 +1,4 @@
-/* Generated by Cython 0.15 on Thu Sep  1 13:51:31 2011 */
+/* Generated by Cython 0.15 on Fri Sep  2 12:00:06 2011 */
 
 #define PY_SSIZE_T_CLEAN
 #include "Python.h"
@@ -756,12 +756,12 @@ static char __pyx_k_197[] = "RandomState.randint (line 809)";
 static char __pyx_k_198[] = "\n        randint(low, high=None, size=None)\n\n        Return random integers from `low` (inclusive) to `high` (exclusive).\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        \"half-open\" interval [`low`, `high`). If `high` is None (the default),\n        then results are from [0, `low`).\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, one above the largest (signed) integer to be drawn\n            from the distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is\n            returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.random_integers : similar to `randint`, only for the closed\n            interval [`low`, `high`], and 1 is the lowest value if `high` is\n            omitted. In particular, this other one is the one to use to generate\n            uniformly distributed discrete non-integers.\n\n        Examples\n        --------\n        >>> np.random.randint(2, size=10)\n        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n        >>> np.random.randint(1, size=10)\n        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n        Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n        >>> np.random.randint(5, size=(2, 4))\n        array([[4, 0, 2, 1],\n               [3, 2, 2, 0]])\n\n        ";
 static char __pyx_k_199[] = "RandomState.bytes (line 889)";
 static char __pyx_k_200[] = "\n        bytes(length)\n\n        Return random bytes.\n\n        Parameters\n        ----------\n        length : int\n            Number of random bytes.\n\n        Returns\n        -------\n        out : str\n            String of length `length`.\n\n        Examples\n        --------\n        >>> np.random.bytes(10)\n        ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n        ";
-static char __pyx_k_201[] = "RandomState.sample (line 917)";
-static char __pyx_k_202[] = "\n        sample(a, size[, replace, p])\n\n        Generates a random sample from a given 1-D array\n\n        Parameters\n        -----------\n        a : 1-D array-like or int\n            If an ndarray, a random sample is generated from its elements.\n            If an int, the random sample is generated as if a was np.arange(n)\n        size : int\n            Positive integer, the size of the sample.\n        replace : boolean, optional\n            Whether the sample is with or without replacement\n        p : 1-D array-like, optional\n            The probabilities associated with each entry in a.\n            If not given the sample assumes a uniform distribtion over all\n            entries in a.\n\n        Returns\n        --------\n        samples : 1-D ndarray, shape (size,)\n            The generated random samples\n\n        Raises\n        -------\n        ValueError\n            If a is an int and less than zero, if a or p are not 1-dimensional,\n            if a is an array-like of size 0, if p is not a vector of\n            probabilities, if a and p have different lengths, or if\n            replace=False and the sample size is greater than the population\n            size\n\n        See Also\n        ---------\n        randint, shuffle, permutation\n\n        Examples\n        ---------\n        Generate a uniform random sample from np.arange(5) of size 3:\n       \n        >>> np.random.sample(5, 3)\n        array([0, 3, 4])\n        >>> #This is equivalent to np.random.randint(0,5,3) \n\n        Generate a non-uniform random sample from np.arange(5) of size 3:\n        \n        >>> np.random.sample(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n        array([3, 3, 0])\n\n        Generate a uniform random sample from np.arange(5) of size 3 without\n        replacement:\n        \n        >>> np.random.sample(5, 3, replace=False)\n        array([3,1,0])\n        >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]\n\n        Generate a non-uniform ra""ndom sample from np.arange(5) of size\n        3 without replacement:\n\n        >>> np.random.sample(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n        array([2, 3, 0])\n\n        Any of the above can be repeated with an arbitrary array-like\n        instead of just integers. For instance:\n\n        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n        >>> np.random.sample(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], \n              dtype='|S11')\n\n        ";
+static char __pyx_k_201[] = "RandomState.choice (line 917)";
+static char __pyx_k_202[] = "\n        choice(a, size=1, replace=True, p=None)\n\n        Generates a random sample from a given 1-D array\n\n        Parameters\n        -----------\n        a : 1-D array-like or int\n            If an ndarray, a random sample is generated from its elements.\n            If an int, the random sample is generated as if a was np.arange(n)\n        size : int\n            Positive integer, the size of the sample.\n        replace : boolean, optional\n            Whether the sample is with or without replacement\n        p : 1-D array-like, optional\n            The probabilities associated with each entry in a.\n            If not given the sample assumes a uniform distribtion over all\n            entries in a.\n\n        Returns\n        --------\n        samples : 1-D ndarray, shape (size,)\n            The generated random samples\n\n        Raises\n        -------\n        ValueError\n            If a is an int and less than zero, if a or p are not 1-dimensional,\n            if a is an array-like of size 0, if p is not a vector of\n            probabilities, if a and p have different lengths, or if\n            replace=False and the sample size is greater than the population\n            size\n\n        See Also\n        ---------\n        randint, shuffle, permutation\n\n        Examples\n        ---------\n        Generate a uniform random sample from np.arange(5) of size 3:\n       \n        >>> np.random.choice(5, 3)\n        array([0, 3, 4])\n        >>> #This is equivalent to np.random.randint(0,5,3) \n\n        Generate a non-uniform random sample from np.arange(5) of size 3:\n        \n        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n        array([3, 3, 0])\n\n        Generate a uniform random sample from np.arange(5) of size 3 without\n        replacement:\n        \n        >>> np.random.choice(5, 3, replace=False)\n        array([3,1,0])\n        >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]\n\n        Generate a non-""uniform random sample from np.arange(5) of size\n        3 without replacement:\n\n        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n        array([2, 3, 0])\n\n        Any of the above can be repeated with an arbitrary array-like\n        instead of just integers. For instance:\n\n        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], \n              dtype='|S11')\n\n        ";
 static char __pyx_k_203[] = "RandomState.uniform (line 1057)";
 static char __pyx_k_204[] = "\n        uniform(low=0.0, high=1.0, size=1)\n\n        Draw samples from a uniform distribution.\n\n        Samples are uniformly distributed over the half-open interval\n        ``[low, high)`` (includes low, but excludes high).  In other words,\n        any value within the given interval is equally likely to be drawn\n        by `uniform`.\n\n        Parameters\n        ----------\n        low : float, optional\n            Lower boundary of the output interval.  All values generated will be\n            greater than or equal to low.  The default value is 0.\n        high : float\n            Upper boundary of the output interval.  All values generated will be\n            less than high.  The default value is 1.0.\n        size : int or tuple of ints, optional\n            Shape of output.  If the given size is, for example, (m,n,k),\n            m*n*k samples are generated.  If no shape is specified, a single sample\n            is returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Discrete uniform distribution, yielding integers.\n        random_integers : Discrete uniform distribution over the closed\n                          interval ``[low, high]``.\n        random_sample : Floats uniformly distributed over ``[0, 1)``.\n        random : Alias for `random_sample`.\n        rand : Convenience function that accepts dimensions as input, e.g.,\n               ``rand(2,2)`` would generate a 2-by-2 array of floats,\n               uniformly distributed over ``[0, 1)``.\n\n        Notes\n        -----\n        The probability density function of the uniform distribution is\n\n        .. math:: p(x) = \\frac{1}{b - a}\n\n        anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> s = np.random.uniform(-1,0,1000)\n\n        All values are w""ithin the given interval:\n\n        >>> np.all(s >= -1)\n        True\n        >>> np.all(s < 0)\n        True\n\n        Display the histogram of the samples, along with the\n        probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
 static char __pyx_k_205[] = "RandomState.rand (line 1144)";
-static char __pyx_k_206[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int\n            Shape of the output.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that\n        takes a shape-tuple as the first argument, refer to\n        `random`.\n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
+static char __pyx_k_206[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int\n            Shape of the output.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that\n        takes a shape-tuple as the first argument, refer to\n        np.random.random_sample . \n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
 static char __pyx_k_207[] = "RandomState.randn (line 1187)";
 static char __pyx_k_208[] = "\n        randn([d1, ..., dn])\n\n        Return a sample (or samples) from the \"standard normal\" distribution.\n\n        If positive, int_like or int-convertible arguments are provided,\n        `randn` generates an array of shape ``(d1, ..., dn)``, filled\n        with random floats sampled from a univariate \"normal\" (Gaussian)\n        distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n        floats, they are first converted to integers by truncation). A single\n        float randomly sampled from the distribution is returned if no\n        argument is provided.\n\n        This is a convenience function.  If you want an interface that takes a\n        tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n        Parameters\n        ----------\n        d1, ..., dn : `n` ints, optional\n            The dimensions of the returned array, should be all positive.\n\n        Returns\n        -------\n        Z : ndarray or float\n            A ``(d1, ..., dn)``-shaped array of floating-point samples from\n            the standard normal distribution, or a single such float if\n            no parameters were supplied.\n\n        See Also\n        --------\n        random.standard_normal : Similar, but takes a tuple as its argument.\n\n        Notes\n        -----\n        For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n        ``sigma * np.random.randn(...) + mu``\n\n        Examples\n        --------\n        >>> np.random.randn()\n        2.1923875335537315 #random\n\n        Two-by-four array of samples from N(3, 6.25):\n\n        >>> 2.5 * np.random.randn(2, 4) + 3\n        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random\n               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random\n\n        ";
 static char __pyx_k_209[] = "RandomState.random_integers (line 1243)";
@@ -889,6 +889,7 @@ static char __pyx_k__shape[] = "shape";
 static char __pyx_k__sigma[] = "sigma";
 static char __pyx_k__zeros[] = "zeros";
 static char __pyx_k__arange[] = "arange";
+static char __pyx_k__choice[] = "choice";
 static char __pyx_k__cumsum[] = "cumsum";
 static char __pyx_k__gumbel[] = "gumbel";
 static char __pyx_k__normal[] = "normal";
@@ -896,7 +897,6 @@ static char __pyx_k__pareto[] = "pareto";
 static char __pyx_k__random[] = "random";
 static char __pyx_k__reduce[] = "reduce";
 static char __pyx_k__repeat[] = "repeat";
-static char __pyx_k__sample[] = "sample";
 static char __pyx_k__uint32[] = "uint32";
 static char __pyx_k__unique[] = "unique";
 static char __pyx_k__MT19937[] = "MT19937";
@@ -1112,6 +1112,7 @@ static PyObject *__pyx_n_s__beta;
 static PyObject *__pyx_n_s__binomial;
 static PyObject *__pyx_n_s__bytes;
 static PyObject *__pyx_n_s__chisquare;
+static PyObject *__pyx_n_s__choice;
 static PyObject *__pyx_n_s__copy;
 static PyObject *__pyx_n_s__cov;
 static PyObject *__pyx_n_s__cumsum;
@@ -1185,7 +1186,6 @@ static PyObject *__pyx_n_s__reduce;
 static PyObject *__pyx_n_s__repeat;
 static PyObject *__pyx_n_s__replace;
 static PyObject *__pyx_n_s__right;
-static PyObject *__pyx_n_s__sample;
 static PyObject *__pyx_n_s__scale;
 static PyObject *__pyx_n_s__searchsorted;
 static PyObject *__pyx_n_s__seed;
@@ -6394,16 +6394,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_11bytes(PyObject *__pyx_v_self,
 }
 
 /* "mtrand.pyx":917
- * 
  *
- *     def sample(self, a, size, replace=True, p=None):             # <<<<<<<<<<<<<<
+ *
+ *     def choice(self, a, size=1, replace=True, p=None):             # <<<<<<<<<<<<<<
  *         """
- *         sample(a, size[, replace, p])
+ *         choice(a, size=1, replace=True, p=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_12sample[] = "\n        sample(a, size[, replace, p])\n\n        Generates a random sample from a given 1-D array\n\n        Parameters\n        -----------\n        a : 1-D array-like or int\n            If an ndarray, a random sample is generated from its elements.\n            If an int, the random sample is generated as if a was np.arange(n)\n        size : int\n            Positive integer, the size of the sample.\n        replace : boolean, optional\n            Whether the sample is with or without replacement\n        p : 1-D array-like, optional\n            The probabilities associated with each entry in a.\n            If not given the sample assumes a uniform distribtion over all\n            entries in a.\n\n        Returns\n        --------\n        samples : 1-D ndarray, shape (size,)\n            The generated random samples\n\n        Raises\n        -------\n        ValueError\n            If a is an int and less than zero, if a or p are not 1-dimensional,\n            if a is an array-like of size 0, if p is not a vector of\n            probabilities, if a and p have different lengths, or if\n            replace=False and the sample size is greater than the population\n            size\n\n        See Also\n        ---------\n        randint, shuffle, permutation\n\n        Examples\n        ---------\n        Generate a uniform random sample from np.arange(5) of size 3:\n       \n        >>> np.random.sample(5, 3)\n        array([0, 3, 4])\n        >>> #This is equivalent to np.random.randint(0,5,3) \n\n        Generate a non-uniform random sample from np.arange(5) of size 3:\n        \n        >>> np.random.sample(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n        array([3, 3, 0])\n\n        Generate a uniform random sample from np.arange(5) of size 3 without\n        replacement:\n        \n        >>> np.random.sample(5, 3, replace=False)\n        array([3,1,0])\n        >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]\n\n        Generate a non-uniform ra""ndom sample from np.arange(5) of size\n        3 without replacement:\n\n        >>> np.random.sample(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n        array([2, 3, 0])\n\n        Any of the above can be repeated with an arbitrary array-like\n        instead of just integers. For instance:\n\n        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n        >>> np.random.sample(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], \n              dtype='|S11')\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_12choice[] = "\n        choice(a, size=1, replace=True, p=None)\n\n        Generates a random sample from a given 1-D array\n\n        Parameters\n        -----------\n        a : 1-D array-like or int\n            If an ndarray, a random sample is generated from its elements.\n            If an int, the random sample is generated as if a was np.arange(n)\n        size : int\n            Positive integer, the size of the sample.\n        replace : boolean, optional\n            Whether the sample is with or without replacement\n        p : 1-D array-like, optional\n            The probabilities associated with each entry in a.\n            If not given the sample assumes a uniform distribtion over all\n            entries in a.\n\n        Returns\n        --------\n        samples : 1-D ndarray, shape (size,)\n            The generated random samples\n\n        Raises\n        -------\n        ValueError\n            If a is an int and less than zero, if a or p are not 1-dimensional,\n            if a is an array-like of size 0, if p is not a vector of\n            probabilities, if a and p have different lengths, or if\n            replace=False and the sample size is greater than the population\n            size\n\n        See Also\n        ---------\n        randint, shuffle, permutation\n\n        Examples\n        ---------\n        Generate a uniform random sample from np.arange(5) of size 3:\n       \n        >>> np.random.choice(5, 3)\n        array([0, 3, 4])\n        >>> #This is equivalent to np.random.randint(0,5,3) \n\n        Generate a non-uniform random sample from np.arange(5) of size 3:\n        \n        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n        array([3, 3, 0])\n\n        Generate a uniform random sample from np.arange(5) of size 3 without\n        replacement:\n        \n        >>> np.random.choice(5, 3, replace=False)\n        array([3,1,0])\n        >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]\n\n        Generate a non-""uniform random sample from np.arange(5) of size\n        3 without replacement:\n\n        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n        array([2, 3, 0])\n\n        Any of the above can be repeated with an arbitrary array-like\n        instead of just integers. For instance:\n\n        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], \n              dtype='|S11')\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_a = 0;
   PyObject *__pyx_v_size = 0;
   PyObject *__pyx_v_replace = 0;
@@ -6431,10 +6431,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
   const char *__pyx_filename = NULL;
   int __pyx_clineno = 0;
   static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__a,&__pyx_n_s__size,&__pyx_n_s__replace,&__pyx_n_s__p,0};
-  __Pyx_RefNannySetupContext("sample");
+  __Pyx_RefNannySetupContext("choice");
   if (unlikely(__pyx_kwds)) {
     Py_ssize_t kw_args = PyDict_Size(__pyx_kwds);
     PyObject* values[4] = {0,0,0,0};
+    values[1] = ((PyObject *)__pyx_int_1);
     values[2] = __pyx_k_15;
     values[3] = ((PyObject *)Py_None);
     switch (PyTuple_GET_SIZE(__pyx_args)) {
@@ -6451,10 +6452,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
       if (likely(values[0])) kw_args--;
       else goto __pyx_L5_argtuple_error;
       case  1:
-      values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-      if (likely(values[1])) kw_args--;
-      else {
-        __Pyx_RaiseArgtupleInvalid("sample", 0, 2, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 917; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      if (kw_args > 0) {
+        PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
+        if (value) { values[1] = value; kw_args--; }
       }
       case  2:
       if (kw_args > 0) {
@@ -6468,32 +6468,30 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
       }
     }
     if (unlikely(kw_args > 0)) {
-      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "sample") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 917; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "choice") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 917; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
     }
     __pyx_v_a = values[0];
     __pyx_v_size = values[1];
     __pyx_v_replace = values[2];
     __pyx_v_p = values[3];
   } else {
+    __pyx_v_size = ((PyObject *)__pyx_int_1);
     __pyx_v_replace = __pyx_k_15;
     __pyx_v_p = ((PyObject *)Py_None);
     switch (PyTuple_GET_SIZE(__pyx_args)) {
-      case  4:
-      __pyx_v_p = PyTuple_GET_ITEM(__pyx_args, 3);
-      case  3:
-      __pyx_v_replace = PyTuple_GET_ITEM(__pyx_args, 2);
-      case  2:
-      __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 1);
-      __pyx_v_a = PyTuple_GET_ITEM(__pyx_args, 0);
+      case  4: __pyx_v_p = PyTuple_GET_ITEM(__pyx_args, 3);
+      case  3: __pyx_v_replace = PyTuple_GET_ITEM(__pyx_args, 2);
+      case  2: __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 1);
+      case  1: __pyx_v_a = PyTuple_GET_ITEM(__pyx_args, 0);
       break;
       default: goto __pyx_L5_argtuple_error;
     }
   }
   goto __pyx_L4_argument_unpacking_done;
   __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("sample", 0, 2, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 917; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("choice", 0, 1, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 917; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
-  __Pyx_AddTraceback("mtrand.RandomState.sample", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __Pyx_AddTraceback("mtrand.RandomState.choice", __pyx_clineno, __pyx_lineno, __pyx_filename);
   __Pyx_RefNannyFinishContext();
   return NULL;
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@@ -6857,7 +6855,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
  *         if replace:
  *             if None != p:             # <<<<<<<<<<<<<<
  *                 x = np.arange(pop_size)
- *                 number_each = np.random.multinomial(size, p)
+ *                 number_each = self.multinomial(size, p)
  */
     __pyx_t_1 = PyObject_RichCompare(Py_None, __pyx_v_p, Py_NE); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1018; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __Pyx_GOTREF(__pyx_t_1);
@@ -6869,7 +6867,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
  *         if replace:
  *             if None != p:
  *                 x = np.arange(pop_size)             # <<<<<<<<<<<<<<
- *                 number_each = np.random.multinomial(size, p)
+ *                 number_each = self.multinomial(size, p)
  *                 idx = np.repeat(x, number_each)
  */
       __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1019; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
@@ -6892,18 +6890,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
       /* "mtrand.pyx":1020
  *             if None != p:
  *                 x = np.arange(pop_size)
- *                 number_each = np.random.multinomial(size, p)             # <<<<<<<<<<<<<<
+ *                 number_each = self.multinomial(size, p)             # <<<<<<<<<<<<<<
  *                 idx = np.repeat(x, number_each)
  *                 self.shuffle(idx)
  */
-      __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1020; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_1 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__random); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1020; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_1);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_3 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__multinomial); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1020; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_3 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__multinomial); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1020; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_3);
-      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
       __pyx_t_1 = PyTuple_New(2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1020; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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       __Pyx_INCREF(__pyx_v_size);
@@ -6921,7 +6913,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
 
       /* "mtrand.pyx":1021
  *                 x = np.arange(pop_size)
- *                 number_each = np.random.multinomial(size, p)
+ *                 number_each = self.multinomial(size, p)
  *                 idx = np.repeat(x, number_each)             # <<<<<<<<<<<<<<
  *                 self.shuffle(idx)
  *             else:
@@ -6947,7 +6939,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
       __pyx_t_3 = 0;
 
       /* "mtrand.pyx":1022
- *                 number_each = np.random.multinomial(size, p)
+ *                 number_each = self.multinomial(size, p)
  *                 idx = np.repeat(x, number_each)
  *                 self.shuffle(idx)             # <<<<<<<<<<<<<<
  *             else:
@@ -7146,7 +7138,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
  *                 p = p.copy()
  *                 found = np.zeros(size, dtype=np.int)             # <<<<<<<<<<<<<<
  *                 while n_uniq < size:
- *                     x = np.random.rand(size-n_uniq)
+ *                     x = self.rand(size-n_uniq)
  */
       __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1035; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_3);
@@ -7179,7 +7171,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
  *                 p = p.copy()
  *                 found = np.zeros(size, dtype=np.int)
  *                 while n_uniq < size:             # <<<<<<<<<<<<<<
- *                     x = np.random.rand(size-n_uniq)
+ *                     x = self.rand(size-n_uniq)
  *                     if n_uniq > 0:
  */
       while (1) {
@@ -7192,18 +7184,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
         /* "mtrand.pyx":1037
  *                 found = np.zeros(size, dtype=np.int)
  *                 while n_uniq < size:
- *                     x = np.random.rand(size-n_uniq)             # <<<<<<<<<<<<<<
+ *                     x = self.rand(size-n_uniq)             # <<<<<<<<<<<<<<
  *                     if n_uniq > 0:
  *                         p[found[0:n_uniq]] = 0
  */
-        __pyx_t_8 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1037; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_8 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__rand); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1037; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
         __Pyx_GOTREF(__pyx_t_8);
-        __pyx_t_4 = PyObject_GetAttr(__pyx_t_8, __pyx_n_s__random); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1037; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_4);
-        __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
-        __pyx_t_8 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__rand); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1037; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_8);
-        __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
         __pyx_t_4 = PyNumber_Subtract(__pyx_v_size, __pyx_v_n_uniq); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1037; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
         __Pyx_GOTREF(__pyx_t_4);
         __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1037; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
@@ -7221,7 +7207,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
 
         /* "mtrand.pyx":1038
  *                 while n_uniq < size:
- *                     x = np.random.rand(size-n_uniq)
+ *                     x = self.rand(size-n_uniq)
  *                     if n_uniq > 0:             # <<<<<<<<<<<<<<
  *                         p[found[0:n_uniq]] = 0
  *                     p = p/p.sum()
@@ -7233,7 +7219,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
         if (__pyx_t_6) {
 
           /* "mtrand.pyx":1039
- *                     x = np.random.rand(size-n_uniq)
+ *                     x = self.rand(size-n_uniq)
  *                     if n_uniq > 0:
  *                         p[found[0:n_uniq]] = 0             # <<<<<<<<<<<<<<
  *                     p = p/p.sum()
@@ -7482,7 +7468,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12sample(PyObject *__pyx_v_self,
   __Pyx_XDECREF(__pyx_t_4);
   __Pyx_XDECREF(__pyx_t_7);
   __Pyx_XDECREF(__pyx_t_8);
-  __Pyx_AddTraceback("mtrand.RandomState.sample", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __Pyx_AddTraceback("mtrand.RandomState.choice", __pyx_clineno, __pyx_lineno, __pyx_filename);
   __pyx_r = NULL;
   __pyx_L0:;
   __Pyx_XDECREF(__pyx_v_pop_size);
@@ -7758,7 +7744,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_13uniform(PyObject *__pyx_v_self
  */
 
 static PyObject *__pyx_pf_6mtrand_11RandomState_14rand(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_14rand[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int\n            Shape of the output.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that\n        takes a shape-tuple as the first argument, refer to\n        `random`.\n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
+static char __pyx_doc_6mtrand_11RandomState_14rand[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int\n            Shape of the output.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that\n        takes a shape-tuple as the first argument, refer to\n        np.random.random_sample . \n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
 static PyObject *__pyx_pf_6mtrand_11RandomState_14rand(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_args = 0;
   PyObject *__pyx_r = NULL;
@@ -19692,7 +19678,7 @@ static PyMethodDef __pyx_methods_6mtrand_RandomState[] = {
   {__Pyx_NAMESTR("tomaxint"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_9tomaxint, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_9tomaxint)},
   {__Pyx_NAMESTR("randint"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_10randint, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_10randint)},
   {__Pyx_NAMESTR("bytes"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_11bytes, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_11bytes)},
-  {__Pyx_NAMESTR("sample"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_12sample, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_12sample)},
+  {__Pyx_NAMESTR("choice"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_12choice, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_12choice)},
   {__Pyx_NAMESTR("uniform"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_13uniform, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_13uniform)},
   {__Pyx_NAMESTR("rand"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_14rand, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_14rand)},
   {__Pyx_NAMESTR("randn"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_15randn, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_15randn)},
@@ -20072,6 +20058,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_n_s__binomial, __pyx_k__binomial, sizeof(__pyx_k__binomial), 0, 0, 1, 1},
   {&__pyx_n_s__bytes, __pyx_k__bytes, sizeof(__pyx_k__bytes), 0, 0, 1, 1},
   {&__pyx_n_s__chisquare, __pyx_k__chisquare, sizeof(__pyx_k__chisquare), 0, 0, 1, 1},
+  {&__pyx_n_s__choice, __pyx_k__choice, sizeof(__pyx_k__choice), 0, 0, 1, 1},
   {&__pyx_n_s__copy, __pyx_k__copy, sizeof(__pyx_k__copy), 0, 0, 1, 1},
   {&__pyx_n_s__cov, __pyx_k__cov, sizeof(__pyx_k__cov), 0, 0, 1, 1},
   {&__pyx_n_s__cumsum, __pyx_k__cumsum, sizeof(__pyx_k__cumsum), 0, 0, 1, 1},
@@ -20145,7 +20132,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_n_s__repeat, __pyx_k__repeat, sizeof(__pyx_k__repeat), 0, 0, 1, 1},
   {&__pyx_n_s__replace, __pyx_k__replace, sizeof(__pyx_k__replace), 0, 0, 1, 1},
   {&__pyx_n_s__right, __pyx_k__right, sizeof(__pyx_k__right), 0, 0, 1, 1},
-  {&__pyx_n_s__sample, __pyx_k__sample, sizeof(__pyx_k__sample), 0, 0, 1, 1},
   {&__pyx_n_s__scale, __pyx_k__scale, sizeof(__pyx_k__scale), 0, 0, 1, 1},
   {&__pyx_n_s__searchsorted, __pyx_k__searchsorted, sizeof(__pyx_k__searchsorted), 0, 0, 1, 1},
   {&__pyx_n_s__seed, __pyx_k__seed, sizeof(__pyx_k__seed), 0, 0, 1, 1},
@@ -22001,9 +21987,9 @@ PyMODINIT_FUNC PyInit_mtrand(void)
   /* "mtrand.pyx":917
  *
  *
- *     def sample(self, a, size, replace=True, p=None):             # <<<<<<<<<<<<<<
+ *     def choice(self, a, size=1, replace=True, p=None):             # <<<<<<<<<<<<<<
  *         """
- *         sample(a, size[, replace, p])
+ *         choice(a, size=1, replace=True, p=None)
  */
   __pyx_t_4 = __Pyx_PyBool_FromLong(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 917; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_4);
@@ -22218,7 +22204,7 @@ PyMODINIT_FUNC PyInit_mtrand(void)
  * get_state = _rand.get_state
  * set_state = _rand.set_state             # <<<<<<<<<<<<<<
  * random_sample = _rand.random_sample
- * sample = _rand.sample
+ * choice = _rand.choice
  */
   __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4473; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_4);
@@ -22232,7 +22218,7 @@ PyMODINIT_FUNC PyInit_mtrand(void)
  * get_state = _rand.get_state
  * set_state = _rand.set_state
  * random_sample = _rand.random_sample             # <<<<<<<<<<<<<<
- * sample = _rand.sample
+ * choice = _rand.choice
  * randint = _rand.randint
  */
   __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4474; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
@@ -22246,21 +22232,21 @@ PyMODINIT_FUNC PyInit_mtrand(void)
   /* "mtrand.pyx":4475
  * set_state = _rand.set_state
  * random_sample = _rand.random_sample
- * sample = _rand.sample             # <<<<<<<<<<<<<<
+ * choice = _rand.choice             # <<<<<<<<<<<<<<
  * randint = _rand.randint
  * bytes = _rand.bytes
  */
   __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4475; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_4);
-  __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__sample); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4475; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__choice); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4475; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_1);
   __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_s__sample, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4475; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_n_s__choice, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4475; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
 
   /* "mtrand.pyx":4476
  * random_sample = _rand.random_sample
- * sample = _rand.sample
+ * choice = _rand.choice
  * randint = _rand.randint             # <<<<<<<<<<<<<<
  * bytes = _rand.bytes
  * uniform = _rand.uniform
@@ -22274,7 +22260,7 @@ PyMODINIT_FUNC PyInit_mtrand(void)
   __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
 
   /* "mtrand.pyx":4477
- * sample = _rand.sample
+ * choice = _rand.choice
  * randint = _rand.randint
  * bytes = _rand.bytes             # <<<<<<<<<<<<<<
  * uniform = _rand.uniform
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index 3772fa07a..3aaefe92e 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -914,9 +914,9 @@ cdef class RandomState:
         return bytestring
 
 
-    def sample(self, a, size, replace=True, p=None):
+    def choice(self, a, size=1, replace=True, p=None):
         """
-        sample(a, size[, replace, p])
+        choice(a, size=1, replace=True, p=None)
 
         Generates a random sample from a given 1-D array
 
@@ -956,33 +956,33 @@ cdef class RandomState:
         ---------
         Generate a uniform random sample from np.arange(5) of size 3:
 
-        >>> np.random.sample(5, 3)
+        >>> np.random.choice(5, 3)
         array([0, 3, 4])
         >>> #This is equivalent to np.random.randint(0,5,3)
 
         Generate a non-uniform random sample from np.arange(5) of size 3:
 
-        >>> np.random.sample(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
+        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
         array([3, 3, 0])
 
         Generate a uniform random sample from np.arange(5) of size 3 without
         replacement:
 
-        >>> np.random.sample(5, 3, replace=False)
+        >>> np.random.choice(5, 3, replace=False)
         array([3,1,0])
         >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]
 
         Generate a non-uniform random sample from np.arange(5) of size
         3 without replacement:
 
-        >>> np.random.sample(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
+        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
         array([2, 3, 0])
 
         Any of the above can be repeated with an arbitrary array-like
         instead of just integers. For instance:
 
         >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
-        >>> np.random.sample(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
+        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
         array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],
               dtype='|S11')
 
@@ -1017,7 +1017,7 @@ cdef class RandomState:
         if replace:
             if None != p:
                 x = np.arange(pop_size)
-                number_each = np.random.multinomial(size, p)
+                number_each = self.multinomial(size, p)
                 idx = np.repeat(x, number_each)
                 self.shuffle(idx)
             else:
@@ -1034,7 +1034,7 @@ cdef class RandomState:
                 p = p.copy()
                 found = np.zeros(size, dtype=np.int)
                 while n_uniq < size:
-                    x = np.random.rand(size-n_uniq)
+                    x = self.rand(size-n_uniq)
                     if n_uniq > 0:
                         p[found[0:n_uniq]] = 0
                     p = p/p.sum()
@@ -4472,7 +4472,7 @@ seed = _rand.seed
 get_state = _rand.get_state
 set_state = _rand.set_state
 random_sample = _rand.random_sample
-sample = _rand.sample
+choice = _rand.choice
 randint = _rand.randint
 bytes = _rand.bytes
 uniform = _rand.uniform