--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tvii/linear_regression.py	Sun Dec 17 14:25:13 2017 -0800
@@ -0,0 +1,130 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Linear regression using tensorflow:
+
+W * x + b = y
+
+Yields `W` and `b`
+
+Ref:
+https://www.tensorflow.org/get_started/get_started
+"""
+
+# minimize:
+# (h(x) - y)**2
+# =>
+# (1./(2*m))*sum_i={i..m} (h(x_i) - y_i)**2
+# m -- number of training examples
+# =>
+# (1./(2*m))*sum_i={i..m} ((b + W*x_i) - y_i)**2
+# : cost function; in this case, the squared error function
+
+import csv
+import json
+import os
+import sys
+import tensorflow as tf
+from .cli import CLIParser as Parser
+from .read import read
+from .transpose import transpose
+
+class LinearRegression(object):
+    """class-based approach to linear regression"""
+
+    def __init__(self, W_guess=0., b_guess=0.):
+        # model parameters: guesses
+        self.W = tf.Variable([W_guess], tf.float32)
+        self.b = tf.Variable([b_guess], tf.float32)
+        raise NotImplementedError('TODO')
+
+    def __call__(self, x_train, y_train, max_iterations=1000):
+        # Model input and output
+        x = tf.placeholder(tf.float32)
+        linear_model = W * x + b
+        y = tf.placeholder(tf.float32)
+
+        # loss
+        loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
+
+
+
+def linear_regression(x_train,
+                      y_train,
+                      W_guess=0.,
+                      b_guess=0.,
+                      max_iterations=1000):
+    """
+    perform a linear regression:
+
+    W*x + b
+
+    Returns:
+    (W_trained, b_trained, loss)
+    """
+
+    # model parameters: guesses
+    W = tf.Variable([W_guess], tf.float32)
+    b = tf.Variable([b_guess], tf.float32)
+
+    # Model input and output
+    x = tf.placeholder(tf.float32)
+    linear_model = W * x + b
+    y = tf.placeholder(tf.float32)
+
+    # loss
+    loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
+
+    # optimizer
+    optimizer = tf.train.GradientDescentOptimizer(0.01)
+    train = optimizer.minimize(loss)
+
+    # training loop
+    init = tf.global_variables_initializer()
+    sess = tf.Session()
+    sess.run(init) # reset values to wrong
+    for i in range(max_iterations):
+        sess.run(train, {x:x_train, y:y_train})
+
+    # evaluate training accuracy
+    curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
+    return (curr_W[0], curr_b[0], curr_loss)
+
+
+def main(args=sys.argv[1:]):
+    """CLI"""
+
+    # parse command line
+    parser = Parser(description=__doc__)
+    parser.add_argument('data', type=read,
+                        help="(x,y) data to read")
+    parser.add_argument('--W', dest='W_guess',
+                        type=float, default=0.3,
+                        help="best guess for `W` [DEFAULT: %(default)s]")
+    parser.add_argument('--b', dest='b_guess',
+                        type=float, default=-0.3,
+                        help="best guess for `b` [DEFAULT: %(default)s]")
+    parser.add_argument('--iterations', '--max-iterations', dest='max_iterations',
+                        type=int, default=1000,
+                        help="maximum number of iterations [DEFAULT: %(default)s]")
+    options = parser.parse_args(args)
+
+    # training data
+    x_train, y_train = transpose(options.data)
+
+    # learn!
+    W, b, loss = linear_regression(x_train,
+                                   y_train,
+                                   options.W_guess,
+                                   options.b_guess,
+                                   max_iterations=options.max_iterations)
+
+    # recite your lesson
+    print (json.dumps(dict(W=float(W),
+                           b=float(b),
+                           loss=float(loss))))
+
+
+if __name__ == '__main__':
+    main()