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[finished]Assignment_3_neural_dependency_parsing/utils/general_utils.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+CS224N 2018-19: Homework 3
+general_utils.py: General purpose utilities.
+Sahil Chopra <schopra8@stanford.edu>
+"""
+
+import sys
+import time
+import numpy as np
+
+
+def get_minibatches(data, minibatch_size, shuffle=True):
+    """
+    Iterates through the provided data one minibatch at at time. You can use this function to
+    iterate through data in minibatches as follows:
+
+        for inputs_minibatch in get_minibatches(inputs, minibatch_size):
+            ...
+
+    Or with multiple data sources:
+
+        for inputs_minibatch, labels_minibatch in get_minibatches([inputs, labels], minibatch_size):
+            ...
+
+    Args:
+        data: there are two possible values:
+            - a list or numpy array
+            - a list where each element is either a list or numpy array
+        minibatch_size: the maximum number of items in a minibatch
+        shuffle: whether to randomize the order of returned data
+    Returns:
+        minibatches: the return value depends on data:
+            - If data is a list/array it yields the next minibatch of data.
+            - If data a list of lists/arrays it returns the next minibatch of each element in the
+              list. This can be used to iterate through multiple data sources
+              (e.g., features and labels) at the same time.
+
+    """
+    list_data = type(data) is list and (type(data[0]) is list or type(data[0]) is np.ndarray)
+    data_size = len(data[0]) if list_data else len(data)
+    indices = np.arange(data_size)
+    if shuffle:
+        np.random.shuffle(indices)
+    for minibatch_start in np.arange(0, data_size, minibatch_size):
+        minibatch_indices = indices[minibatch_start:minibatch_start + minibatch_size]
+        yield [_minibatch(d, minibatch_indices) for d in data] if list_data \
+            else _minibatch(data, minibatch_indices)
+
+
+def _minibatch(data, minibatch_idx):
+    return data[minibatch_idx] if type(data) is np.ndarray else [data[i] for i in minibatch_idx]
+
+
+def test_all_close(name, actual, expected):
+    if actual.shape != expected.shape:
+        raise ValueError("{:} failed, expected output to have shape {:} but has shape {:}"
+                         .format(name, expected.shape, actual.shape))
+    if np.amax(np.fabs(actual - expected)) > 1e-6:
+        raise ValueError("{:} failed, expected {:} but value is {:}".format(name, expected, actual))
+    else:
+        print(name, "passed!")