implementing normal.py.cs API log_prob() for NB classifier prediction · attackgithub/TensorFlow.NET@d19e513 · GitHub

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implementing normal.py.cs API log_prob() for NB classifier prediction

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4 files changed

+55-4Lines changed: 55 additions & 4 deletions

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• src/TensorFlowNET.Core/Operations
  • Distributions
    • distribution.py.cs
    • normal.py.cs
  • math_ops.py.cs
• test/TensorFlowNET.Examples
  • NaiveBayesClassifier.cs

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‎src/TensorFlowNET.Core/Operations/Distributions/distribution.py.cs‎

Copy file name to clipboardExpand all lines: src/TensorFlowNET.Core/Operations/Distributions/distribution.py.cs

+36-1Lines changed: 36 additions & 1 deletion

Original file line number	Diff line number	Diff line change
@@ -27,6 +27,37 @@ public class Distribution : _BaseDistribution
27	27	public List<Tensor> _graph_parents {get;set;}
28	28	public string _name {get;set;}
29	29
	30	+
	31	+ /// <summary>
	32	+ /// Log probability density/mass function.
	33	+ /// </summary>
	34	+ /// <param name="value"> `Tensor`.</param>
	35	+ /// <param name="name"> Python `str` prepended to names of ops created by this function.</param>
	36	+ /// <returns>log_prob: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type `self.dtype`.</returns>
	37	+
	38	+ /*
	39	+ public Tensor log_prob(Tensor value, string name = "log_prob")
	40	+ {
	41	+ return _call_log_prob(value, name);
	42	+ }
	43	+
	44	+ private Tensor _call_log_prob (Tensor value, string name)
	45	+ {
	46	+ with(ops.name_scope(name, "moments", new { value }), scope =>
	47	+ {
	48	+ value = _convert_to_tensor(value, "value", _dtype);
	49	+ });
	50	+
	51	+ throw new NotImplementedException();
	52	+
	53	+ }
	54	+
	55	+ private Tensor _convert_to_tensor(Tensor value, string name = null, TF_DataType preferred_dtype)
	56	+ {
	57	+ throw new NotImplementedException();
	58	+ }
	59	+ */
	60	+
30	61	/// <summary>
31	62	/// Constructs the `Distribution'
32	63	/// **This is a private method for subclass use.**
@@ -47,7 +78,7 @@ public class Distribution : _BaseDistribution
47	78	/// <param name = "name"> Name prefixed to Ops created by this class. Default: subclass name.</param>
48	79	/// <returns> Two `Tensor` objects: `mean` and `variance`.</returns>
49	80
50		- /*
	81	+ /*
51	82	private Distribution (
52	83	TF_DataType dtype,
53	84	ReparameterizationType reparameterization_type,
@@ -66,6 +97,10 @@ private Distribution (
66	97	this._name = name;
67	98	}
68	99	*/
	100	+
	101	+
	102	+
	103	+
69	104	}
70	105
71	106	/// <summary>

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‎src/TensorFlowNET.Core/Operations/Distributions/normal.py.cs‎

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+14Lines changed: 14 additions & 0 deletions

Original file line number	Diff line number	Diff line change
@@ -78,5 +78,19 @@ public Tensor _batch_shape()
78	78	return array_ops.broadcast_static_shape(new Tensor(_loc.shape), new Tensor(_scale.shape));
79	79	}
80	80
	81	+ private Tensor _log_prob(Tensor x)
	82	+ {
	83	+ return _log_unnormalized_prob(_z(x));
	84	+ }
	85	+
	86	+ private Tensor _log_unnormalized_prob (Tensor x)
	87	+ {
	88	+ return -0.5 * math_ops.square(_z(x));
	89	+ }
	90	+
	91	+ private Tensor _z (Tensor x)
	92	+ {
	93	+ return (x - this._loc) / this._scale;
	94	+ }
81	95	}
82	96	}

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‎src/TensorFlowNET.Core/Operations/math_ops.py.cs‎

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+5Lines changed: 5 additions & 0 deletions

Original file line number	Diff line number	Diff line change
@@ -55,6 +55,11 @@ public static Tensor square_difference(Tensor x, Tensor y, string name = null)
55	55	return m;
56	56	}
57	57
	58	+ public static Tensor square(Tensor x, string name = null)
	59	+ {
	60	+ throw new NotImplementedException();
	61	+ }
	62	+
58	63	/// <summary>
59	64	/// Helper function for reduction ops.
60	65	/// </summary>

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‎test/TensorFlowNET.Examples/NaiveBayesClassifier.cs‎

Copy file name to clipboardExpand all lines: test/TensorFlowNET.Examples/NaiveBayesClassifier.cs

-3Lines changed: 0 additions & 3 deletions

Original file line number	Diff line number	Diff line change
@@ -15,13 +15,10 @@ public class NaiveBayesClassifier : Python, IExample
15	15	public void Run()
16	16	{
17	17	np.array<float>(1.0f, 1.0f);
18		- // var X = np.array<float>(np.array<float>(1.0f, 1.0f), np.array<float>(2.0f, 2.0f), np.array<float>(1.0f, -1.0f), np.array<float>(2.0f, -2.0f), np.array<float>(-1.0f, -1.0f), np.array<float>(-1.0f, 1.0f),);
19		- // var X = np.array<float[]>(new float[][] { new float[] { 1.0f, 1.0f}, new float[] { 2.0f, 2.0f }, new float[] { -1.0f, -1.0f }, new float[] { -2.0f, -2.0f }, new float[] { 1.0f, -1.0f }, new float[] { 2.0f, -2.0f }, });
20	18	var X = np.array<float>(new float[][] { new float[] { 1.0f, 1.0f }, new float[] { 2.0f, 2.0f }, new float[] { -1.0f, -1.0f }, new float[] { -2.0f, -2.0f }, new float[] { 1.0f, -1.0f }, new float[] { 2.0f, -2.0f }, });
21	19	var y = np.array<int>(0,0,1,1,2,2);
22	20	fit(X, y);
23	21	// Create a regular grid and classify each point
24		-
25	22	}
26	23
27	24	public void fit(NDArray X, NDArray y)

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