@webwriter/neural-network
Version:
Deep learning visualization for feed-forward networks with custom datasets, training and prediction.
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text/typescript
import { LitElementWw } from '@webwriter/lit'
import { TemplateResult, html } from 'lit'
import { customElement } from 'lit/decorators.js'
import { consume } from '@lit/context'
import { CEdge } from '@/components/network/c_edge'
import type { SelectedEle } from '@/types/selected_ele'
import { selectedEleContext } from '@/contexts/selected_ele_context'
import { CPanel } from '../reusables/c-panel'
import { EdgeInfoCard } from '@/components/cards/edge_info_card'
import { EdgeWeightCard } from '@/components/cards/edge_weight_card'
export class EdgePanel extends LitElementWw {
static scopedElements = {
"c-panel": CPanel,
"edge-info-card": EdgeInfoCard,
"edge-weight-card": EdgeWeightCard
}
({ context: selectedEleContext, subscribe: true })
accessor selectedEle: SelectedEle
// RENDER - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
render(): TemplateResult<1> {
if (this.selectedEle && this.selectedEle instanceof CEdge) {
const edge: CEdge = this.selectedEle
return html`
<c-panel name="edge">
<edge-info-card .source=${edge.source} .target=${edge.target}>
</edge-info-card>
${edge.weight
? html`<edge-weight-card .weight=${edge.weight}></edge-weight-card>`
: html``}
</c-panel>
`
}
}
}