Updated DenseVector description in CS120x review lab

spark-mooc · Jul 14, 2016 · 27c6484 · 27c6484
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diff --git a/cs120_autograder_complete.dbc b/cs120_autograder_complete.dbc
diff --git a/cs120_autograder_complete.py b/cs120_autograder_complete.py
@@ -1,7 +1,7 @@
 # Databricks notebook source exported at Tue, 21 Jun 2016 17:18:30 UTC
 
 # MAGIC %md
-# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0"src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/></a><br/>This work is licensed under a<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 InternationalLicense</a>.
+# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/> </a> <br/> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. </a>
 
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diff --git a/cs120_autograder_register.dbc b/cs120_autograder_register.dbc
diff --git a/cs120_autograder_register.py b/cs120_autograder_register.py
@@ -1,7 +1,7 @@
 # Databricks notebook source exported at Mon, 11 Jul 2016 16:37:17 UTC
 
 # MAGIC %md
-# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0"src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/></a><br/>This work is licensed under a<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 InternationalLicense</a>.
+# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/> </a> <br/> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. </a>
 
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diff --git a/cs120_autograder_simpler.dbc b/cs120_autograder_simpler.dbc
diff --git a/cs120_autograder_simpler.py b/cs120_autograder_simpler.py
@@ -1,7 +1,7 @@
 # Databricks notebook source exported at Mon, 11 Jul 2016 17:51:44 UTC
 
 # MAGIC %md
-# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0"src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/></a><br/>This work is licensed under a<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 InternationalLicense</a>.
+# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/> </a> <br/> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. </a>
 
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diff --git a/cs120_lab0.dbc b/cs120_lab0.dbc
diff --git a/cs120_lab0.py b/cs120_lab0.py
@@ -1,7 +1,7 @@
 # Databricks notebook source exported at Sun, 10 Jul 2016 19:49:03 UTC
 
 # MAGIC %md
-# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0"src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/></a><br/>This work is licensed under a<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 InternationalLicense</a>.
+# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/> </a> <br/> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. </a>
 
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diff --git a/cs120_lab1a_math_review.dbc b/cs120_lab1a_math_review.dbc
diff --git a/cs120_lab1a_math_review.py b/cs120_lab1a_math_review.py
@@ -1,7 +1,7 @@
-# Databricks notebook source exported at Mon, 11 Jul 2016 16:58:17 UTC
+# Databricks notebook source exported at Thu, 14 Jul 2016 00:59:32 UTC
 
 # MAGIC %md
-# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0"src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/></a><br/>This work is licensed under a<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 InternationalLicense</a>.
+# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/> </a> <br/> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. </a>
 
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@@ -327,12 +327,10 @@
 # MAGIC %md
 # MAGIC ### (3c) PySpark's DenseVector
 # MAGIC 
-# MAGIC In frequent ML scenarios, you may end up with very long vectors, possibly 100k's to millions, where most of the values are zeroes.  PySpark provides a [DenseVector](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.DenseVector) class (in module the module [pyspark.mllib.linalg](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#module-pyspark.mllib.linalg)), which allows you to more efficiently operate and store these sparse vectors.
-# MAGIC 
-# MAGIC `DenseVector` is used to store arrays of values for use in PySpark.  `DenseVector` actually stores values in a NumPy array and delegates calculations to that object.  You can create a new `DenseVector` using `DenseVector()` and passing in a NumPy array or a Python list.
+# MAGIC PySpark provides a [DenseVector](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.DenseVector) class within the module [pyspark.mllib.linalg](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#module-pyspark.mllib.linalg).  `DenseVector` is used to store arrays of values for use in PySpark.  `DenseVector` actually stores values in a NumPy array and delegates calculations to that object.  You can create a new `DenseVector` using `DenseVector()` and passing in an NumPy array or a Python list.
 # MAGIC 
 # MAGIC `DenseVector` implements several functions.  The only function needed for this course is `DenseVector.dot()`, which operates just like `np.ndarray.dot()`.
-# MAGIC Note that `DenseVector` stores all values as `np.float64`, so even if you pass in an NumPy array of integers, the resulting `DenseVector` will contain floating-point numbers. Also, `DenseVector` objects exist locally and are not inherently distributed.  `DenseVector` objects can be used in the distributed setting by either passing functions that contain them to resilient distributed dataset (RDD) transformations or by distributing them directly as RDDs.  You'll learn more about RDDs in the spark tutorial.
+# MAGIC Note that `DenseVector` stores all values as `np.float64`, so even if you pass in an NumPy array of integers, the resulting `DenseVector` will contain floating-point numbers. Also, `DenseVector` objects exist locally and are not inherently distributed.  `DenseVector` objects can be used in the distributed setting by either passing functions that contain them to resilient distributed dataset (RDD) transformations or by distributing them directly as RDDs.
 # MAGIC 
 # MAGIC For this exercise, create a `DenseVector` consisting of the values `[3.0, 4.0, 5.0]` and compute the dot product of this vector with `numpyVector`.
 

diff --git a/cs120_lab1b_word_count_rdd.dbc b/cs120_lab1b_word_count_rdd.dbc
diff --git a/cs120_lab1b_word_count_rdd.py b/cs120_lab1b_word_count_rdd.py
@@ -1,7 +1,7 @@
 # Databricks notebook source exported at Fri, 8 Jul 2016 18:23:19 UTC
 
 # MAGIC %md
-# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0"src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/></a><br/>This work is licensed under a<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 InternationalLicense</a>.
+# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/> </a> <br/> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. </a>
 
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diff --git a/src/cs120x/cs120_lab1a_math_review.py b/src/cs120x/cs120_lab1a_math_review.py
@@ -1,4 +1,4 @@
-# Databricks notebook source exported at Mon, 11 Jul 2016 16:58:17 UTC
+# Databricks notebook source exported at Thu, 14 Jul 2016 00:59:32 UTC
 # MAGIC %md
 # MAGIC ![ML Logo](http://spark-mooc.github.io/web-assets/images/CS190.1x_Banner_300.png)
 # MAGIC # Math and Python review
@@ -481,12 +481,10 @@
 # MAGIC %md
 # MAGIC ### (3c) PySpark's DenseVector
 # MAGIC 
-# MAGIC In frequent ML scenarios, you may end up with very long vectors, possibly 100k's to millions, where most of the values are zeroes.  PySpark provides a [DenseVector](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.DenseVector) class (in module the module [pyspark.mllib.linalg](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#module-pyspark.mllib.linalg)), which allows you to more efficiently operate and store these sparse vectors.
-# MAGIC 
-# MAGIC `DenseVector` is used to store arrays of values for use in PySpark.  `DenseVector` actually stores values in a NumPy array and delegates calculations to that object.  You can create a new `DenseVector` using `DenseVector()` and passing in a NumPy array or a Python list.
+# MAGIC PySpark provides a [DenseVector](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.DenseVector) class within the module [pyspark.mllib.linalg](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#module-pyspark.mllib.linalg).  `DenseVector` is used to store arrays of values for use in PySpark.  `DenseVector` actually stores values in a NumPy array and delegates calculations to that object.  You can create a new `DenseVector` using `DenseVector()` and passing in an NumPy array or a Python list.
 # MAGIC 
 # MAGIC `DenseVector` implements several functions.  The only function needed for this course is `DenseVector.dot()`, which operates just like `np.ndarray.dot()`.
-# MAGIC Note that `DenseVector` stores all values as `np.float64`, so even if you pass in an NumPy array of integers, the resulting `DenseVector` will contain floating-point numbers. Also, `DenseVector` objects exist locally and are not inherently distributed.  `DenseVector` objects can be used in the distributed setting by either passing functions that contain them to resilient distributed dataset (RDD) transformations or by distributing them directly as RDDs.  You'll learn more about RDDs in the spark tutorial.
+# MAGIC Note that `DenseVector` stores all values as `np.float64`, so even if you pass in an NumPy array of integers, the resulting `DenseVector` will contain floating-point numbers. Also, `DenseVector` objects exist locally and are not inherently distributed.  `DenseVector` objects can be used in the distributed setting by either passing functions that contain them to resilient distributed dataset (RDD) transformations or by distributing them directly as RDDs.
 # MAGIC 
 # MAGIC For this exercise, create a `DenseVector` consisting of the values `[3.0, 4.0, 5.0]` and compute the dot product of this vector with `numpyVector`.
 
@@ -1003,4 +1001,4 @@ def __str__(self):  return 'FunctionalWrapper({0})'.format(str(self.data))
 # MAGIC %md
 # MAGIC ### <img src="http://spark-mooc.github.io/web-assets/images/oops.png" style="height: 200px"/> If things go wrong
 # MAGIC 
-# MAGIC It's possible that your notebook looks fine to you, but fails in the autograder. (This can happen when you run cells out of order, as you're working on your notebook.) If that happens, just try again, starting at the top of Appendix A.
+# MAGIC It's possible that your notebook looks fine to you, but fails in the autograder. (This can happen when you run cells out of order, as you're working on your notebook.) If that happens, just try again, starting at the top of Appendix A.