Merge pull request #71 from ealcobaca/online-doc-fix

Online doc fix

Author: ealcobaca

docs/source/_static/theme_overrides.css

@@ -0,0 +1,13 @@
+/* override table width restrictions */
+@media screen and (min-width: 767px) {
+
+   .wy-table-responsive table td {
+      /* !important prevents the common CSS stylesheets from overriding
+         this as on RTD they are loaded after this stylesheet */
+      white-space: normal !important;
+   }
+
+   .wy-table-responsive {
+      overflow: visible !important;
+   }
+}

examples/01_introductory_examples/plot_groups.py

@@ -76,7 +76,7 @@
 # measures:
 
 ###############################################################################
-# Extract all info-theo measures
+# Extract all info-theory measures
 mfe = MFE(groups=["info-theory"])
 mfe.fit(X, y)
 ft = mfe.extract()
@@ -109,7 +109,7 @@
 
 ###############################################################################
 # Extract only two model-based measures
-mfe = MFE(features=["leaves", "nodel"])
+mfe = MFE(features=["leaves", "nodes"])
 mfe.fit(X, y)
 ft = mfe.extract()
 print("\n".join("{:50} {:30}".format(x, y) for x, y in zip(ft[0], ft[1])))
@@ -167,7 +167,7 @@
 # dataset.
 
 ###############################################################################
-# Extract all relative landmarking measures
+# Extract all subsampling landmarking measures
 mfe = MFE(groups=["landmarking"], lm_sample_frac=0.7)
 mfe.fit(X, y)
 ft = mfe.extract()
@@ -196,7 +196,7 @@
 # the examples density.
 
 ###############################################################################
-# Extract all clustering based measures
+# Extract all concept measures
 mfe = MFE(groups=["concept"])
 mfe.fit(X, y)
 ft = mfe.extract()
@@ -210,7 +210,7 @@
 #
 
 ###############################################################################
-# Extract all clustering based measures
+# Extract all itemset measures
 mfe = MFE(groups=["itemset"])
 mfe.fit(X, y)
 ft = mfe.extract()
@@ -225,7 +225,7 @@
 #
 
 ###############################################################################
-# Extract all clustering based measures
+# Extract all complexity measures
 mfe = MFE(groups=["complexity"])
 mfe.fit(X, y)
 ft = mfe.extract()