Merge pull request #13 from AutoResearch/docs-sampler-to-experimentalist

docs: sampler to experimentalist at the appropriate places

README.md

@@ -12,7 +12,7 @@ You will need:
 - `graphviz` (optional, required for computation graph visualizations): 
   [https://graphviz.org/download/](https://graphviz.org/download/)
 
-Install the inequality sampler as part of the `autora` package:
+Install the inequality experimentalist as part of the `autora` package:
 
 ```shell
 pip install -U "autora[experimentalist-inequality]"

docs/Basic Usage.ipynb

@@ -8,7 +8,7 @@
    },
    "source": [
     "# Basic Usage\n",
-    "The inequality sampler selects $n$ experimental conditions from a pool of candidate experimental conditions $X'$. The choice is informed based on the similarity of the candidate conditions $X'$ with respect to previously examined experiment conditions $X$.\n",
+    "The inequality experimentalist selects $n$ experimental conditions from a pool of candidate experimental conditions $X'$. The choice is informed based on the similarity of the candidate conditions $X'$ with respect to previously examined experiment conditions $X$.\n",
     "We begin with importing the relevant packages."
    ]
   },
@@ -100,7 +100,7 @@
     "collapsed": false
    },
    "source": [
-    "Finally, we can call the inequality sampler. Note that $X'$ is the first argument to the sampler, followed by the \"reference\" conditions $X$, and the number of samples."
+    "Finally, we can call the inequality experimentalist. Note that $X'$ is the first argument to the experimentalist, followed by the \"reference\" conditions $X$, and the number of samples."
    ]
   },
   {
@@ -128,7 +128,7 @@
     "collapsed": false
    },
    "source": [
-    "The novelty sampler also works for experiments with multiple indendent variables. In the following example, we define $X$ as a single experimental condition composed of three independent factors. We choose from a pool $X'$ composed of four experimental conditons."
+    "The inequality experimentalist also works for experiments with multiple independent variables. In the following example, we define $X$ as a single experimental condition composed of three independent factors. We choose from a pool $X'$ composed of four experimental conditions."
    ]
   },
   {

docs/index.md

@@ -9,7 +9,7 @@ Given:
 - A threshold value (default = 0) that determines the maximum allowable distance for two conditions to be considered equal.
 - A number $n$ of conditions to sample.
 
-The inequality sampler operates as follows:
+The inequality experimentalist operates as follows:
 
 1. For each candidate condition $\vec{x}'$ in $X'$ calculate an $inequality$ $score$:
 2. Calculate the distances $d(\vec{x}, \vec{x}')$ between $\vec{x}$ and $\vec{x}'$ using the pairwise distance metric for all $\vec{x}$ in $X$.

docs/quickstart.md

@@ -4,7 +4,7 @@ You will need:
 
 - `python` 3.8 or greater: [https://www.python.org/downloads/](https://www.python.org/downloads/)
 
-*Inequality sampler* is a part of the `autora` package:
+*Inequality experimentalist* is a part of the `autora` package:
 
 ```shell
 pip install -U autora[experimentalist-inequality]

src/autora/experimentalist/inequality/__init__.py

@@ -37,7 +37,7 @@ def sample(
     metric: str = "euclidean",
 ) -> np.ndarray:
     """
-    This inequality sampler chooses from the pool of IV conditions according to their
+    This inequality experimentalist chooses from the pool of IV conditions according to their
     inequality with respect to a reference pool reference_conditions. Two IVs are considered
     equal if their distance is less than the equality_distance. The IVs chosen first are feed back
     into reference_conditions and are included in the summed equality calculation.
@@ -70,13 +70,13 @@ def sample(
         >>> summed_inequality_sampler([1, 2, 3], [1, 1, 1, 2, 2, 2, 3, 3])
         array([[3]])
 
-        The samplers "fills up" the reference array so the values are contributed evenly
+        The experimentalist "fills up" the reference array so the values are contributed evenly
         >>> summed_inequality_sampler([1, 1, 1, 2, 2, 2, 3, 3, 3], [1, 1, 2, 2, 2, 2, 3, 3, 3], 3)
         array([[1],
                [3],
                [1]])
 
-        The sampler samples without replacemnt!
+        The experimentalist samples without replacemnt!
         >>> summed_inequality_sampler([1, 2, 3], [1, 1, 1], 3)
         array([[3],
                [2],