diff --git a/learning.py b/learning.py
index 24554ff22..bc2ce6cfb 100644
--- a/learning.py
+++ b/learning.py
@@ -120,7 +120,7 @@ def setproblem(self, target, inputs=None, exclude=()):
self.check_me()
def check_me(self):
- "Check that my fields make sense."
+ """Check that my fields make sense."""
assert len(self.attrnames) == len(self.attrs)
assert self.target in self.attrs
assert self.target not in self.inputs
@@ -130,12 +130,12 @@ def check_me(self):
list(map(self.check_example, self.examples))
def add_example(self, example):
- "Add an example to the list of examples, checking it first."
+ """Add an example to the list of examples, checking it first."""
self.check_example(example)
self.examples.append(example)
def check_example(self, example):
- "Raise ValueError if example has any invalid values."
+ """Raise ValueError if example has any invalid values."""
if self.values:
for a in self.attrs:
if example[a] not in self.values[a]:
@@ -143,7 +143,7 @@ def check_example(self, example):
.format(example[a], self.attrnames[a], example))
def attrnum(self, attr):
- "Returns the number used for attr, which can be a name, or -n .. n-1."
+ """Returns the number used for attr, which can be a name, or -n .. n-1."""
if isinstance(attr, str):
return self.attrnames.index(attr)
elif attr < 0:
@@ -152,7 +152,7 @@ def attrnum(self, attr):
return attr
def sanitize(self, example):
- "Return a copy of example, with non-input attributes replaced by None."
+ """Return a copy of example, with non-input attributes replaced by None."""
return [attr_i if i in self.inputs else None
for i, attr_i in enumerate(example)]
@@ -165,12 +165,11 @@ def __repr__(self):
def parse_csv(input, delim=','):
r"""Input is a string consisting of lines, each line has comma-delimited
- fields. Convert this into a list of lists. Blank lines are skipped.
+ fields. Convert this into a list of lists. Blank lines are skipped.
Fields that look like numbers are converted to numbers.
The delim defaults to ',' but '\t' and None are also reasonable values.
>>> parse_csv('1, 2, 3 \n 0, 2, na')
- [[1, 2, 3], [0, 2, 'na']]
- """
+ [[1, 2, 3], [0, 2, 'na']]"""
lines = [line for line in input.splitlines() if line.strip()]
return [list(map(num_or_str, line.split(delim))) for line in lines]
@@ -199,7 +198,7 @@ def __init__(self, observations=[], default=0):
self.add(o)
def add(self, o):
- "Add an observation o to the distribution."
+ """Add an observation o to the distribution."""
self.smooth_for(o)
self.dictionary[o] += 1
self.n_obs += 1
@@ -214,18 +213,18 @@ def smooth_for(self, o):
self.sampler = None
def __getitem__(self, item):
- "Return an estimate of the probability of item."
+ """Return an estimate of the probability of item."""
self.smooth_for(item)
return self.dictionary[item] / self.n_obs
# (top() and sample() are not used in this module, but elsewhere.)
def top(self, n):
- "Return (count, obs) tuples for the n most frequent observations."
+ """Return (count, obs) tuples for the n most frequent observations."""
return heapq.nlargest(n, [(v, k) for (k, v) in self.dictionary.items()])
def sample(self):
- "Return a random sample from the distribution."
+ """Return a random sample from the distribution."""
if self.sampler is None:
self.sampler = weighted_sampler(list(self.dictionary.keys()),
list(self.dictionary.values()))
@@ -240,7 +239,7 @@ def PluralityLearner(dataset):
most_popular = mode([e[dataset.target] for e in dataset.examples])
def predict(example):
- "Always return same result: the most popular from the training set."
+ """Always return same result: the most popular from the training set."""
return most_popular
return predict
@@ -278,9 +277,9 @@ def class_probability(targetval):
def NearestNeighborLearner(dataset, k=1):
- "k-NearestNeighbor: the k nearest neighbors vote."
+ """k-NearestNeighbor: the k nearest neighbors vote."""
def predict(example):
- "Find the k closest, and have them vote for the best."
+ """Find the k closest items, and have them vote for the best."""
best = heapq.nsmallest(k, ((dataset.distance(e, example), e)
for e in dataset.examples))
return mode(e[dataset.target] for (d, e) in best)
@@ -295,18 +294,18 @@ class DecisionFork:
of branches, one for each of the attribute's values."""
def __init__(self, attr, attrname=None, branches=None):
- "Initialize by saying what attribute this node tests."
+ """Initialize by saying what attribute this node tests."""
self.attr = attr
self.attrname = attrname or attr
self.branches = branches or {}
def __call__(self, example):
- "Given an example, classify it using the attribute and the branches."
+ """Given an example, classify it using the attribute and the branches."""
attrvalue = example[self.attr]
return self.branches[attrvalue](example)
def add(self, val, subtree):
- "Add a branch. If self.attr = val, go to the given subtree."
+ """Add a branch. If self.attr = val, go to the given subtree."""
self.branches[val] = subtree
def display(self, indent=0):
@@ -323,7 +322,7 @@ def __repr__(self):
class DecisionLeaf:
- "A leaf of a decision tree holds just a result."
+ """A leaf of a decision tree holds just a result."""
def __init__(self, result):
self.result = result
@@ -341,7 +340,7 @@ def __repr__(self):
def DecisionTreeLearner(dataset):
- "[Figure 18.5]"
+ """[Figure 18.5]"""
target, values = dataset.target, dataset.values
@@ -369,21 +368,21 @@ def plurality_value(examples):
return DecisionLeaf(popular)
def count(attr, val, examples):
- "Count the number of examples that have attr = val."
+ """Count the number of examples that have attr = val."""
return len(e[attr] == val for e in examples) #count(e[attr] == val for e in examples)
def all_same_class(examples):
- "Are all these examples in the same target class?"
+ """Are all these examples in the same target class?"""
class0 = examples[0][target]
return all(e[target] == class0 for e in examples)
def choose_attribute(attrs, examples):
- "Choose the attribute with the highest information gain."
+ """Choose the attribute with the highest information gain."""
return argmax_random_tie(attrs,
key=lambda a: information_gain(a, examples))
def information_gain(attr, examples):
- "Return the expected reduction in entropy from splitting by attr."
+ """Return the expected reduction in entropy from splitting by attr."""
def I(examples):
return information_content([count(target, v, examples)
for v in values[target]])
@@ -393,7 +392,7 @@ def I(examples):
return I(examples) - remainder
def split_by(attr, examples):
- "Return a list of (val, examples) pairs for each val of attr."
+ """Return a list of (val, examples) pairs for each val of attr."""
return [(v, [e for e in examples if e[attr] == v])
for v in values[attr]]
@@ -401,7 +400,7 @@ def split_by(attr, examples):
def information_content(values):
- "Number of bits to represent the probability distribution in values."
+ """Number of bits to represent the probability distribution in values."""
probabilities = normalize(removeall(0, values))
return sum(-p * math.log2(p) for p in probabilities)
@@ -427,11 +426,11 @@ def find_examples(examples):
raise NotImplementedError
def passes(example, test):
- "Does the example pass the test?"
+ """Does the example pass the test?"""
raise NotImplementedError
def predict(example):
- "Predict the outcome for the first passing test."
+ """Predict the outcome for the first passing test."""
for test, outcome in predict.decision_list:
if passes(example, test):
return outcome
@@ -447,7 +446,7 @@ def NeuralNetLearner(dataset, hidden_layer_sizes=[3],
"""
Layered feed-forward network.
hidden_layer_sizes: List of number of hidden units per hidden layer
- learning_rate: Learning rate of gradient decent
+ learning_rate: Learning rate of gradient descent
epoches: Number of passes over the dataset
"""
@@ -487,7 +486,7 @@ class NNUnit:
"""
Single Unit of Multiple Layer Neural Network
inputs: Incoming connections
- weights: weights to incoming connections
+ weights: Weights to incoming connections
"""
def __init__(self, weights=None, inputs=None):
@@ -500,7 +499,7 @@ def __init__(self, weights=None, inputs=None):
def network(input_units, hidden_layer_sizes, output_units):
"""
Create Directed Acyclic Network of given number layers.
- hidden_layers_sizes : list number of neuron units in each hidden layer
+ hidden_layers_sizes : List number of neuron units in each hidden layer
excluding input and output layers
"""
# Check for PerceptronLearner
@@ -627,8 +626,8 @@ def predict(example):
# ______________________________________________________________________________
-def Linearlearner(dataset, learning_rate=0.01, epochs=100):
- """Define with learner = Linearlearner(data); infer with learner(x)."""
+def LinearLearner(dataset, learning_rate=0.01, epochs=100):
+ """Define with learner = LinearLearner(data); infer with learner(x)."""
idx_i = dataset.inputs
idx_t = dataset.target # As of now, dataset.target gives only one index.
examples = dataset.examples
@@ -702,7 +701,7 @@ def train(dataset):
def WeightedMajority(predictors, weights):
- "Return a predictor that takes a weighted vote."
+ """Return a predictor that takes a weighted vote."""
def predict(example):
return weighted_mode((predictor(example) for predictor in predictors),
weights)
@@ -712,7 +711,8 @@ def predict(example):
def weighted_mode(values, weights):
"""Return the value with the greatest total weight.
>>> weighted_mode('abbaa', [1,2,3,1,2])
- 'b'"""
+ 'b'
+ """
totals = defaultdict(int)
for v, w in zip(values, weights):
totals[v] += w
@@ -731,7 +731,7 @@ def train(dataset, weights):
def replicated_dataset(dataset, weights, n=None):
- "Copy dataset, replicating each example in proportion to its weight."
+ """Copy dataset, replicating each example in proportion to its weight."""
n = n or len(dataset.examples)
result = copy.copy(dataset)
result.examples = weighted_replicate(dataset.examples, weights, n)
@@ -743,7 +743,8 @@ def weighted_replicate(seq, weights, n):
seq proportional to the corresponding weight (filling in fractions
randomly).
>>> weighted_replicate('ABC', [1,2,1], 4)
- ['A', 'B', 'B', 'C']"""
+ ['A', 'B', 'B', 'C']
+ """
assert len(seq) == len(weights)
weights = normalize(weights)
wholes = [int(w * n) for w in weights]
@@ -759,7 +760,7 @@ def flatten(seqs): return sum(seqs, [])
def test(predict, dataset, examples=None, verbose=0):
- "Return the proportion of the examples that are NOT correctly predicted."
+ """Return the proportion of the examples that are NOT correctly predicted."""
if examples is None:
examples = dataset.examples
if len(examples) == 0:
@@ -791,7 +792,7 @@ def train_and_test(dataset, start, end):
def cross_validation(learner, size, dataset, k=10, trials=1):
"""Do k-fold cross_validate and return their mean.
That is, keep out 1/k of the examples for testing on each of k runs.
- Shuffle the examples first; If trials>1, average over several shuffles.
+ Shuffle the examples first; if trials>1, average over several shuffles.
Returns Training error, Validataion error"""
if k is None:
k = len(dataset.examples)
@@ -824,11 +825,11 @@ def cross_validation(learner, size, dataset, k=10, trials=1):
def cross_validation_wrapper(learner, dataset, k=10, trials=1):
"""
- Fig 18.8
+ [Fig 18.8]
Return the optimal value of size having minimum error
on validataion set.
- err_train: a training error array, indexed by size
- err_val: a validataion error array, indexed by size
+ err_train: A training error array, indexed by size
+ err_val: A validataion error array, indexed by size
"""
err_val = []
err_train = []
@@ -847,7 +848,7 @@ def cross_validation_wrapper(learner, dataset, k=10, trials=1):
def leave_one_out(learner, dataset):
- "Leave one out cross-validation over the dataset."
+ """Leave one out cross-validation over the dataset."""
return cross_validation(learner, size, dataset, k=len(dataset.examples))
@@ -882,7 +883,7 @@ def score(learner, size):
def RestaurantDataSet(examples=None):
- "Build a DataSet of Restaurant waiting examples. [Figure 18.3]"
+ """Build a DataSet of Restaurant waiting examples. [Figure 18.3]"""
return DataSet(name='restaurant', target='Wait', examples=examples,
attrnames='Alternate Bar Fri/Sat Hungry Patrons Price ' +
'Raining Reservation Type WaitEstimate Wait')
@@ -921,7 +922,7 @@ def T(attrname, branches):
def SyntheticRestaurant(n=20):
- "Generate a DataSet with n examples."
+ """Generate a DataSet with n examples."""
def gen():
example = list(map(random.choice, restaurant.values))
example[restaurant.target] = waiting_decision_tree(example)
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