@@ -122,7 +122,7 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
122122 Neuron cookSurnameEntity = Neuron.init(
123123 m.createNeuron("E-cook (surname)"),
124124 3.0, // adjusts the bias
125- new Input () // Requires the word to be recognized
125+ new Synapse.Builder () // Requires the word to be recognized
126126 .setNeuron(inputNeurons.get("cook"))
127127 .setWeight(10.0)
128128 // This input requires the input activation to have an
@@ -132,7 +132,7 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
132132 .setRecurrent(false)
133133 .setRangeMatch(EQUALS)
134134 .setRangeOutput(true), // propagate the range of this input
135- new Input () // The previous word needs to be a forename
135+ new Synapse.Builder () // The previous word needs to be a forename
136136 .setNeuron(forenameCategory)
137137 .setWeight(10.0)
138138 .setBias(-9.0)
@@ -143,7 +143,7 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
143143 // This neuron may be suppressed by the E-cook (profession) neuron, but there is no
144144 // self suppression taking place even though 'E-cook (surname)' is also contained
145145 // in suppressingN.
146- new Input ()
146+ new Synapse.Builder ()
147147 .setNeuron(suppressingN)
148148 .setWeight(-20.0)
149149 .setBias(0.0)
@@ -159,7 +159,7 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
159159 < pre class ="prettyprint ">
160160 < code class ="language-java ">
161161 Neuron.init(forenameCategory, 0.0,
162- new Input () // In this example, only one forename is considered.
162+ new Synapse.Builder () // In this example, only one forename is considered.
163163 .setNeuron(jacksonForenameEntity)
164164 .setWeight(10.0)
165165 .setBias(0.0)
@@ -168,7 +168,7 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
168168 .setRangeOutput(true)
169169 );
170170 Neuron.init(surnameCategory, 0.0,
171- new Input ()
171+ new Synapse.Builder ()
172172 .setNeuron(cookSurnameEntity)
173173 .setWeight(10.0)
174174 .setBias(0.0)
@@ -185,25 +185,25 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
185185 < pre class ="prettyprint ">
186186 < code class ="language-java ">
187187 Neuron.init(suppressingN, 0.0,
188- new Input ()
188+ new Synapse.Builder ()
189189 .setNeuron(cookProfessionEntity)
190190 .setWeight(10.0)
191191 .setBias(0.0)
192192 .setRangeMatch(EQUALS)
193193 .setRangeOutput(true),
194- new Input ()
194+ new Synapse.Builder ()
195195 .setNeuron(cookSurnameEntity)
196196 .setWeight(10.0)
197197 .setBias(0.0)
198198 .setRangeMatch(EQUALS)
199199 .setRangeOutput(true),
200- new Input ()
200+ new Synapse.Builder ()
201201 .setNeuron(jacksonCityEntity)
202202 .setWeight(10.0)
203203 .setBias(0.0)
204204 .setRangeMatch(EQUALS)
205205 .setRangeOutput(true),
206- new Input ()
206+ new Synapse.Builder ()
207207 .setNeuron(jacksonForenameEntity)
208208 .setWeight(10.0)
209209 .setBias(0.0)
@@ -323,14 +323,14 @@ <h3>Mutual exclusion example</h3>
323323 Neuron pA = Neuron.init(
324324 m.createNeuron("A"),
325325 3.0,
326- new Input ()
326+ new Synapse.Builder ()
327327 .setNeuron(inA)
328328 .setWeight(10.0)
329329 .setBias(-10.0)
330330 .setRecurrent(false)
331331 .setRangeMatch(EQUALS)
332332 .setRangeOutput(true),
333- new Input () // This input is negated
333+ new Synapse.Builder () // This input is negated
334334 .setNeuron(pSuppr)
335335 .setWeight(-10.0)
336336 .setBias(0.0)
@@ -341,14 +341,14 @@ <h3>Mutual exclusion example</h3>
341341 Neuron pB = Neuron.init(
342342 m.createNeuron("B"),
343343 5.0,
344- new Input ()
344+ new Synapse.Builder ()
345345 .setNeuron(inB)
346346 .setWeight(10.0)
347347 .setBias(-10.0)
348348 .setRecurrent(false)
349349 .setRangeMatch(EQUALS)
350350 .setRangeOutput(true),
351- new Input ()
351+ new Synapse.Builder ()
352352 .setNeuron(pSuppr)
353353 .setWeight(-10.0)
354354 .setBias(0.0)
@@ -359,14 +359,14 @@ <h3>Mutual exclusion example</h3>
359359 Neuron pC = Neuron.init(
360360 m.createNeuron("C"),
361361 2.0,
362- new Input ()
362+ new Synapse.Builder ()
363363 .setNeuron(inC)
364364 .setWeight(10.0)
365365 .setBias(-10.0)
366366 .setRecurrent(false)
367367 .setRangeMatch(EQUALS)
368368 .setRangeOutput(true),
369- new Input ()
369+ new Synapse.Builder ()
370370 .setNeuron(pSuppr)
371371 .setWeight(-10.0)
372372 .setBias(0.0)
@@ -378,22 +378,21 @@ <h3>Mutual exclusion example</h3>
378378 Neuron.init(
379379 pSuppr,
380380 0.0,
381- new Input ()
381+ new Synapse.Builder ()
382382 .setNeuron(pA)
383383 .setWeight(10.0)
384384 .setBias(0.0)
385385 .setRecurrent(false)
386- .setMinInput(1.0)
387386 .setRangeMatch(EQUALS)
388387 .setRangeOutput(true),
389- new Input ()
388+ new Synapse.Builder ()
390389 .setNeuron(pB)
391390 .setWeight(10.0)
392391 .setBias(0.0)
393392 .setRecurrent(false)
394393 .setRangeMatch(EQUALS)
395394 .setRangeOutput(true),
396- new Input ()
395+ new Synapse.Builder ()
397396 .setNeuron(pC)
398397 .setWeight(10.0)
399398 .setBias(0.0)
@@ -403,7 +402,7 @@ <h3>Mutual exclusion example</h3>
403402 );
404403
405404 Neuron outN = Neuron.init(new Neuron("OUT"), 0.0,
406- new Input ()
405+ new Synapse.Builder ()
407406 .setNeuron(pB)
408407 .setWeight(1.0)
409408 .setBias(0.0)
@@ -532,7 +531,7 @@ <h3>Pattern matching example</h3>
532531 Neuron pattern = Neuron.init(
533532 m.createNeuron("BCDE"),
534533 0.4,
535- new Input ()
534+ new Synapse.Builder ()
536535 .setNeuron(inputNeurons.get('b'))
537536 .setWeight(1.0)
538537 .setBias(-0.9)
@@ -541,21 +540,21 @@ <h3>Pattern matching example</h3>
541540 .setStartRangeMatch(Operator.EQUALS)
542541 .setEndRangeMatch(Operator.GREATER_THAN)
543542 .setStartRangeOutput(true),
544- new Input ()
543+ new Synapse.Builder ()
545544 .setNeuron(inputNeurons.get('c'))
546545 .setWeight(1.0)
547546 .setBias(-0.9)
548547 .setRecurrent(false)
549548 .setRelativeRid(1)
550549 .setRangeMatch(RangeRelation.CONTAINS),
551- new Input ()
550+ new Synapse.Builder ()
552551 .setNeuron(inputNeurons.get('d'))
553552 .setWeight(1.0)
554553 .setBias(-0.9)
555554 .setRecurrent(false)
556555 .setRelativeRid(2)
557556 .setRangeMatch(RangeRelation.CONTAINS),
558- new Input ()
557+ new Synapse.Builder ()
559558 .setNeuron(inputNeurons.get('e'))
560559 .setWeight(1.0)
561560 .setBias(-0.9)
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