small typos

README.md

@@ -35,7 +35,10 @@ Moreover, negative Poisson log-likelihood is a more appropriate loss for count d
 ## Installation
 
 Package is available on PyPI, can be installed with:
-```pip install hpfrec```
+
+```
+pip install hpfrec
+```
 
 As it contains Cython code, it requires a C compiler. In Windows, this usually means it requires a Visual Studio installation (or MinGW + GCC), and if using Anaconda, might also require configuring it to use said Visual Studio instead of MinGW, otherwise the installation from `pip` might fail. For more details see this guide:
 [Cython Extensions On Windows](https://github.com/cython/cython/wiki/CythonExtensionsOnWindows)

example/hpfrec_echonest.ipynb

@@ -63,7 +63,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np, pandas as pd\n",
@@ -629,7 +631,9 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "train = pd.merge(train, song_info, left_on='ItemId', right_on='SongId')"
@@ -1586,17 +1590,6 @@
     "* Gopalan, Prem, Jake M. Hofman, and David M. Blei. \"Scalable Recommendation with Hierarchical Poisson Factorization.\" UAI. 2015.\n",
     "* Gopalan, Prem, Jake M. Hofman, and David M. Blei. \"Scalable recommendation with poisson factorization.\" arXiv preprint arXiv:1311.1704 (2013)."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle\n",
-    "\n",
-    "pickle.dump(recommender, open('rec.p','wb'))"
-   ]
   }
  ],
  "metadata": {

setup.py

@@ -19,8 +19,8 @@
 ### Comment: -Ofast gives worse speed than -O2 or -O3
 
 setup(
-  name = 'fneighcf',
-  packages = ['fneighcf'],
+  name = 'hpfrec',
+  packages = ['hpfrec'],
   install_requires=[
    'pandas>=0.21',
    'numpy',