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yalmansour1998
@ -1,18 +1,14 @@
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import asyncio
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import dash
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import plotly.graph_objects as go
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from dash import html, dcc, callback, Input, Output, State
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from dash import html, dcc, callback, Input, Output
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import dash_bootstrap_components as dbc
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from gng2 import GrowingNeuralGas
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import numpy as np
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from GNG_3D import *
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from sklearn import datasets as sk
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from pages.ascan import data_traits
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dash.register_page(
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__name__, path="/gng", title="GNG", name="GNG", description="Growing Neural Gas"
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)
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# Generate synthetic data
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X, Y = sk.make_moons(n_samples=200, noise=0.1, random_state=0)
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dash.register_page(__name__, path="/gng", title="GNG", name="GNG")
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clics = None
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@ -27,13 +23,25 @@ layout = html.Div(
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),
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dbc.Col(
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[
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dbc.Label("Noise: "),
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dbc.Label("seuil: "),
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dbc.Input(
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placeholder="Noise",
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value=0.1,
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placeholder="seuil",
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value=100,
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type="number",
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id="noise",
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step=0.1,
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id="seuil",
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step=1,
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),
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]
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),
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dbc.Col(
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[
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dbc.Label("max neurons: "),
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dbc.Input(
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placeholder="max_neurons",
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value=1000,
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type="number",
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id="max_neurons",
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step=10,
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),
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]
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),
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@ -41,133 +49,165 @@ layout = html.Div(
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[
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dbc.Label("Iterations: "),
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dbc.Input(
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placeholder="Iterations",
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placeholder="Iteration",
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value=1000,
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type="number",
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id="iterations",
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id="Iterations",
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step=10,
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),
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]
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),
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dbc.Col(
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[
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dbc.Label("Max Nodes: "),
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dbc.Label("Max_age: "),
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dbc.Input(
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placeholder="Max Nodes",
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value=200,
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placeholder="Max_of_age",
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value=5,
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type="number",
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id="nodes",
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id="Max_of_age",
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step=1,
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),
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]
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),
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dbc.Col(
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[
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dbc.Label("distance entre le neurone le plus proche et le signal d'entrée: "),
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dbc.Input(
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placeholder="eb",
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value=0.1,
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type="number",
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id="eb",
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step=0.1,
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),
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]
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),
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dbc.Col(
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[
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dbc.Label("distance entre les neurones voisins et le signal d'entrée: "),
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dbc.Input(
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placeholder="en",
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value=0.1,
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type="number",
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id="en",
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step=0.1,
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),
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]
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),
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dbc.Col(
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[
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dbc.Label("l'erreur locale: "),
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dbc.Input(
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placeholder="alpha",
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value=0.1,
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type="number",
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id="alpha",
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step=0.1,
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),
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]
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),
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dbc.Col(
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[
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dbc.Label("l'erreur globale: "),
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dbc.Input(
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placeholder="beta",
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value=0.1,
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type="number",
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id="beta",
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step=0.1,
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),
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]
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),
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dbc.Col(
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[
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dbc.Label("L: "),
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dbc.Input(
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placeholder="l",
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value=0.1,
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type="number",
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id="l",
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step=0.1,
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),
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]
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),
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]
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),
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dcc.Graph(id="base-graph"),
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#dcc.Graph(id="base-graph"),
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dcc.Graph(id="gng-graph"),
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]
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)
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@callback(
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Output("base-graph", "figure", allow_duplicate=True),
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[Input("noise", "value")],
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prevent_initial_call=True,
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)
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def update_base_graph(noise):
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X, Y = sk.make_moons(n_samples=200, noise=noise, random_state=0)
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fig = go.Figure()
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fig.add_trace(
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go.Scatter(
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x=X[Y == 0, 0], y=X[Y == 0, 1], mode="markers", marker=dict(color="blue")
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)
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)
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fig.add_trace(
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go.Scatter(
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x=X[Y == 1, 0], y=X[Y == 1, 1], mode="markers", marker=dict(color="red")
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)
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)
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fig.update_layout(
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showlegend=False,
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margin=dict(l=0, r=0, t=0, b=0),
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xaxis=dict(visible=False),
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yaxis=dict(visible=False),
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title="Base Data",
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)
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return fig
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@callback(
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[Output("gng-graph", "figure"), Output("base-graph", "figure")],
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[Output("gng-graph", "figure")],
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[
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Input("generate-gng", "n_clicks"),
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Input("seuil", "value"),
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Input("max_neurons", "value"),
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Input("Iterations", "value"),
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Input("Max_of_age", "value"),
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Input("eb", "value"),
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Input("en", "value"),
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Input("alpha", "value"),
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Input("beta", "value"),
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Input("l", "value"),
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],
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[State("noise", "value"), State("iterations", "value"), State("nodes", "value")],
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)
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def generate_gng(n_clicks, noise, iterations, nodes):
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global clics
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X, Y = sk.make_moons(n_samples=200, noise=noise, random_state=0)
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def generate_gng(n_clicks, seuil, max_neurons, max_iter, max_age, eb, en, alpha, beta, l):
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fig2 = go.Figure()
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fig2.add_trace(
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go.Scatter(
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x=X[Y == 0, 0], y=X[Y == 0, 1], mode="markers", marker=dict(color="blue")
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)
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)
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fig2.add_trace(
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go.Scatter(
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x=X[Y == 1, 0], y=X[Y == 1, 1], mode="markers", marker=dict(color="red")
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)
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)
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fig2.update_layout(
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showlegend=False,
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margin=dict(l=0, r=0, t=0, b=0),
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xaxis=dict(visible=False),
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yaxis=dict(visible=False),
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title="Base Data",
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)
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global clics
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if n_clicks != clics:
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gng = GrowingNeuralGas(input_dim=2, max_nodes=nodes)
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asyncio.run(gng.fit(X, num_iterations=iterations))
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data=np.array(data_traits)
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fig = go.Figure()
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for edge in gng.edges:
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fig.add_trace(
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go.Scatter(
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x=[edge.nodes[0].position[0], edge.nodes[1].position[0]],
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y=[edge.nodes[0].position[1], edge.nodes[1].position[1]],
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mode="lines",
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line=dict(width=2, color="white"),
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)
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)
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for node in gng.nodes:
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fig.add_trace(
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go.Scatter(
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x=[node.position[0]],
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y=[node.position[1]],
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mode="markers",
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marker=dict(size=10, color="red"),
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)
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)
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fig.update_layout(
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showlegend=False,
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margin=dict(l=0, r=0, t=0, b=0),
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xaxis=dict(visible=False),
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yaxis=dict(visible=False),
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title="GNG Model",
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)
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size_data=np.shape(data)
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x1=[]
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x2=[]
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x3=[]
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x4=[]
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for i in range(0,size_data[0]):
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res=data[i,:,:]
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res=position_point_espace(data=res,seuil=200,y=i)
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res = np.array(res)
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x1.append(res)
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gng = GrowingNeuralGas(int(max_neurons),int(max_iter),int(max_age), float(eb), float(en), float(alpha), float(beta),int(l), res)
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gng_graph = gng.learn()
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neuron_positions = np.array([vertex['weight'] for vertex in gng_graph.vs])
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neuron_edge= np.array([(edge.source, edge.target) for edge in gng_graph.es])
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for edge in gng_graph.es:
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src, tgt = edge.source, edge.target
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src_pos, tgt_pos = gng_graph.vs[src]['weight'], gng_graph.vs[tgt]['weight']
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x3.append(src_pos)
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x4.append(tgt_pos)
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x2.append(neuron_positions)
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res=np.vstack(x1)
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neuron_positions=np.vstack(x2)
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src_pos=np.vstack(x3)
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tgt_pos=np.vstack(x4)
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fig.add_trace(go.Scatter3d(
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x=res[:, 0],
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y=res[:, 1],
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z=res[:, 2],
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mode='markers',
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marker=dict(size=1, color=res[:, 3], colorscale='Viridis', opacity=0.5),
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name='Data'
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))
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fig.add_trace(go.Scatter3d(
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x=neuron_positions[:, 0],
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y=neuron_positions[:, 1],
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z=neuron_positions[:, 2],
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mode='markers',
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marker=dict(size=4, color=neuron_positions[:,3]),
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name='Neurons'
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))
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for src, tgt in zip(src_pos, tgt_pos):
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fig.add_trace(go.Scatter3d(
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x=[src[0], tgt[0]],
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y=[src[1], tgt[1]],
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z=[src[2], tgt[2]],
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mode='lines',
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line=dict(color="black", width=1),
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showlegend=False,
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))
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clics = n_clicks
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return [fig, fig2]
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return [
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go.Figure().update_layout(
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showlegend=False,
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margin=dict(l=0, r=0, t=0, b=0),
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xaxis=dict(visible=False),
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yaxis=dict(visible=False),
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),
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fig2,
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]
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return [fig]
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