update
This commit is contained in:
parent
40671c8432
commit
dfa27657f5
|
|
@ -110,7 +110,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.13"
|
||||
"version": "3.9.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -2,37 +2,154 @@
|
|||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 1,
|
||||
"id": "011c5f34-be40-4f0b-8146-73f66ba18672",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from optimization.common import *\n",
|
||||
"from optimization.gd_new import*"
|
||||
"from optimization.gradient import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 2,
|
||||
"id": "302d9c60",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "fragment"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "047719f4c6674039838c1b45b32bb5b4",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"VBox(children=(HBox(children=(Text(value='(x - 2)**2 + 3', description='Expression:', style=TextStyle(descript…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "f6602e1e61e04f58bbcfa2f4325a814b",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "564ae8a816f64bbd90c3b6b961b18706",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<optimization.common.funcPlot1d at 0x17bd394f0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"funcPlot1d(environ=\"jupyterlab\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 3,
|
||||
"id": "a4827f0c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "f3047bfdf60a41e69222e33b1fe216ef",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"VBox(children=(HBox(children=(VBox(children=(Text(value='sin(x) + sin((10.0 / 3.0) * x)', description='Express…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "014093558f9141699a4ea4dfe285afeb",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "0c7fc5eac02345af8210d61c64ec1d0b",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"a = gd_1d(environ=\"jupyterlab\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "79f3577e-1e5a-46d8-a4d7-c1a45dcaaa07",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"a = gd_1d(environ=\"jupyterlab\")"
|
||||
"xrange = np.linspace(np.array(a.xn_list)-2, np.array(a.xn_list)+2, 10)\n",
|
||||
"xrange[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "47c012d9-2544-4f2e-8d1d-aec2ae03551a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"np.array(a.xn_list)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -60,21 +177,7 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"gd_2d(environ=\"jupyterlab\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3c737f58-0d7a-4e55-b50e-ec1e538c3822",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"expr = \"(1 - 8 * x1 + 7 * x1**2 - (7/3) * x1**3 + (1/4) * x1**4) * x2**2 * E**(-x2)\"\n",
|
||||
"xn = np.array([0, 2])\n",
|
||||
"x1 = symbols('x1')\n",
|
||||
"x2 = symbols('x2')\n",
|
||||
"expr = sympify(expr)"
|
||||
"gd2d(environ=\"jupyterlab\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -90,53 +193,10 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": null,
|
||||
"id": "d5651fc9-3fcd-4e91-8d3c-04897da1ea02",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "21388df7b7514d58a0e413beff1985ce",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"VBox(children=(HBox(children=(Dropdown(options=(('(1 - 8 * x1 + 7 * x1**2 - (7/3) * x1**3 + (1/4) * x1**4) * x…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "83d82890dadb4e2eafc36993ff5acbe8",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "e7df9fb42125432a884d262cf6a8e5b3",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Output()"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from optimization.gradient import *\n",
|
||||
"a = gd2d_compete(environ=\"jupyterlab\")"
|
||||
|
|
@ -148,6 +208,53 @@
|
|||
"id": "3ce3b0fa-5813-49dd-90de-b28c5d3faf46",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"expr = a.wg_expr.value"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "93a54c5c-02f4-47ed-90cb-ef348d1333be",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x = symbols(\"x\")\n",
|
||||
"expr = sympify(a.wg_expr.value)\n",
|
||||
"f = lambdify(x, sympify(expr), \"numpy\")\n",
|
||||
"f_xn = f(np.array(a.xn_list))\n",
|
||||
"\n",
|
||||
"xrange = np.linspace(np.array(a.xn_list)[0]-1, np.array(a.xn_list)[0]+1, 10)\n",
|
||||
"tangent_line = a.df_list[0] * (x - np.array(a.xn_list[0])) + f_xn[0]\n",
|
||||
"lambdify(x, tangent_line)(xrange)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "26e9ef27-5dcb-4bb3-8c30-301bc7303dcd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"a.df_list[0] * (x - np.array(a.xn_list[0])) + f_xn[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "40ad9639-89fd-4296-8d5c-08f7476546d7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"a.df_list[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "00387a2e-f4e1-431d-8b7f-e2b4b75679ab",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
|
|
@ -168,7 +275,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.13"
|
||||
"version": "3.9.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -78,7 +78,6 @@ class gd_1d(object):
|
|||
f = lambdify(x, expr)
|
||||
df = lambdify(x, diff(expr, x))
|
||||
self.xn_list, self.df_list = [], []
|
||||
|
||||
for n in tqdm(range(0, self.wg_max_iter.value)):
|
||||
gradient = df(xn)
|
||||
self.xn_list.append(xn)
|
||||
|
|
@ -105,13 +104,25 @@ class gd_1d(object):
|
|||
fx = f(xx1)
|
||||
f_xn = f(np.array(self.xn_list))
|
||||
|
||||
tangent_x, tangent_y = [], []
|
||||
for i in range(0, len(f_xn)):
|
||||
xrange = np.linspace(np.array(self.xn_list)[i]-0.5, np.array(self.xn_list)[i]+0.5, 10)
|
||||
tangent_line = self.df_list[i] * (x - np.array(self.xn_list)[i]) + f_xn[i]
|
||||
tangent_x.append(xrange)
|
||||
tangent_y.append(lambdify(x, tangent_line)(xrange))
|
||||
|
||||
fig = go.Figure()
|
||||
fig.add_scatter(x=xx1, y=fx)
|
||||
frames = []
|
||||
frames.append({'data':copy.deepcopy(fig['data']),'name':f'frame{0}'})
|
||||
fig.add_traces(go.Scatter(x=None, y=None, mode="lines + markers", line={"color":"#de1032", "width":1, 'dash': 'dash'}))
|
||||
frames = [go.Frame(data= [go.Scatter(x=np.array(self.xn_list)[:k], y=f_xn)],traces= [1],name=f'frame{k+2}')for k in range(len(f_xn))]
|
||||
fig.update(frames=frames)
|
||||
#fig.add_scatter(x=xx1, y=fx)
|
||||
|
||||
fig.add_trace(go.Scatter(x=xx1, y=fx))
|
||||
fig.add_traces(go.Scatter(x=None, y=None, mode="lines + markers", line={"color":"#de1032", "width":3, 'dash': 'dash'}))
|
||||
fig.add_traces(go.Scatter(x=None, y=None, mode="lines", line={"color":"#debc10", "width":3, 'dash': 'dash'}))
|
||||
|
||||
frames = [go.Frame(data=[go.Scatter(x=xx1, y=fx),
|
||||
go.Scatter(x=np.array(self.xn_list)[:k], y=f_xn),
|
||||
go.Scatter(x=tangent_x[k], y=tangent_y[k])],
|
||||
traces= [0, 1, 2]) for k in range(len(f_xn))]
|
||||
fig.frames = frames
|
||||
fig.update_layout(height=800, updatemenus=[dict(type="buttons",buttons=[dict(label="Play",method="animate",args=[None, dict(fromcurrent=True, transition=dict(duration=0), frame=dict(redraw=True, duration=1000))])])])
|
||||
fig.show()
|
||||
|
||||
|
|
@ -220,17 +231,17 @@ class gd2d(object):
|
|||
|
||||
fig = make_subplots(rows=1, cols=2, specs=[[{'type': 'surface'}, {'type': 'surface'}]])
|
||||
fig.add_trace(go.Surface(contours = {"x": {"show": True}, "y":{"show": True}, "z":{"show": True}}, x=xx1, y=xx2, z=fx), row=1, col=1)
|
||||
fig.add_trace(go.Scatter3d(x=None, y=None, z=None), row=1, col=1)
|
||||
fig.add_trace(go.Scatter3d(x=None, y=None, z=None, marker=dict(size=5)), row=1, col=1)
|
||||
fig.add_trace(go.Surface(x=None, y=None, z=None, showlegend=False, showscale=False, colorscale='Blues'), row=1, col=1)
|
||||
fig.add_trace(go.Surface(z=list(z_offset), x=xx1, y=xx2, showlegend=False, showscale=False, surfacecolor=colorsurfz), row=1, col=2)
|
||||
fig.add_trace(go.Scatter3d(x=None, y=None, z=None), row=1, col=2)
|
||||
fig.add_trace(go.Scatter3d(x=None, y=None, z=None), row=1, col=2)
|
||||
fig.add_trace(go.Scatter3d(x=None, y=None, z=None, marker=dict(size=5)), row=1, col=2)
|
||||
fig.add_trace(go.Scatter3d(x=None, y=None, z=None, marker=dict(size=5)), row=1, col=2)
|
||||
frames = [go.Frame(data=[go.Surface(visible=True, showscale=False, opacity=0.8),
|
||||
go.Scatter3d(x=np.array(self.xn_list)[:k,0], y=np.array(self.xn_list)[:k,1], z=f_xn, line={"color":"#10dedb", "width":3, 'dash': 'dash'}),
|
||||
go.Scatter3d(x=np.array(self.xn_list)[:k,0], y=np.array(self.xn_list)[:k,1], z=f_xn, marker=dict(size=5), line={"color":"#10dedb", "width":3, 'dash': 'dash'}),
|
||||
go.Surface(visible=False, x=xx1_tangent, y=xx2_tangent, z=z[k]),
|
||||
go.Surface(visible=True, showscale=False, opacity=0.8),
|
||||
go.Scatter3d(x=np.array(self.xn_list)[:k, 0], y=np.array(self.xn_list)[:k, 1], z=f_xn, line={"color":"#10dedb", "width":3, 'dash': 'dash'}),
|
||||
go.Scatter3d(x=np.array(self.xn_list)[:k, 0].flatten(), y=np.array(self.xn_list)[:k, 1].flatten(), z=z_offset.flatten(), line={"color":"#58de10", "width":3, 'dash': 'dash'})],
|
||||
go.Scatter3d(x=np.array(self.xn_list)[:k, 0].flatten(), y=np.array(self.xn_list)[:k, 1].flatten(), z=z_offset.flatten(), marker=dict(size=5), line={"color":"#58de10", "width":3, 'dash': 'dash'})],
|
||||
traces=[0, 1, 2, 3, 4, 5]) for k in range(len(f_xn))]
|
||||
fig.frames = frames
|
||||
self.fig_frames = frames
|
||||
|
|
@ -353,10 +364,10 @@ class gd2d_compete(object):
|
|||
fig = make_subplots(rows=1, cols=1, specs=[[{'type': 'surface'}]])
|
||||
fig.add_trace(go.Surface(x=xx1, y=xx2, z=fx), row=1, col=1)
|
||||
fig.add_trace(go.Scatter3d(x=np.array(self.xn_p0_list)[:, 0], y=np.array(self.xn_p0_list)[:, 1], z=fx_p0,
|
||||
name="candidate 1", mode="lines+markers", marker=dict(color="green")), row=1, col=1)
|
||||
name="candidate 1", mode="lines+markers", marker=dict(size=5, color="green")), row=1, col=1)
|
||||
fig.add_trace(go.Scatter3d(x=np.array(self.xn_p1_list)[:, 0], y=np.array(self.xn_p1_list)[:, 1], z=fx_p1,
|
||||
name="candidate 2", mode="lines+markers", marker=dict(color="blue")), row=1, col=1)
|
||||
frames = [go.Frame(data = [go.Surface(visible=True, showscale=False, opacity=0.8),
|
||||
name="candidate 2", mode="lines+markers", marker=dict(size=5, color="blue")), row=1, col=1)
|
||||
frames = [go.Frame(data = [go.Surface(visible=True, showscale=False, opacity=0.6),
|
||||
go.Scatter3d(x=np.array(self.xn_p0_list)[:self.timer, 0], y=np.array(self.xn_p0_list)[:self.timer, 1], z=fx_p0),
|
||||
go.Scatter3d(x=np.array(self.xn_p1_list)[:self.timer, 0], y=np.array(self.xn_p1_list)[:self.timer, 1], z=fx_p1)],
|
||||
traces=[0,1,2])]
|
||||
|
|
|
|||
Loading…
Reference in New Issue