update

algorithm/interactive.ipynb

@@ -12,34 +12,120 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "0a5c51d2-8b18-4143-b6f1-73a909ccb623",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bcac263607a46d19fead8b1cf79e498",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(VBox(children=(Text(value='x**3 - x**(1/2)', description='Expression:', style=Te…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f64c031482d41799291390431aa3264",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0504d25c3aab477ca0acf3d054dbaf92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "gd1 = gradient_descent_1d(environ=\"jupyterlab\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "15c6e757-cde3-422b-be7e-3f55b7752142",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fca48b98af304f1d821c948d8dcc8629",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(Dropdown(index=2, options=(('(sin(x1) - 2) + (sin(x2) - 2) ** 2', '(sin(x1) - 2)…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f25a00c1bb294530973475c7a5c8bb10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4afc98f7c6954a58aba389526ba09164",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "gd2 = gradient_descent_2d(environ=\"jupyterlab\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "694b7e27-cc21-44f4-9a89-7a6b97725a64",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "08df46f6a3b54543a15dd79d71c334bc",
+       "model_id": "f1d170fccfee4e5891a3c33ad564c197",
        "version_major": 2,
        "version_minor": 0
       },
@@ -53,7 +139,21 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee2bc3a3c2b843aeae594ece6e02596e",
+       "model_id": "e6a5a98d84b54da7b1ac6deaa408ed5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b35d3e6383947cfb24f1663388ef556",
        "version_major": 2,
        "version_minor": 0
       },
@@ -69,33 +169,10 @@
     "gd2_race = gradient_descent_2d_race(environ=\"jupyterlab\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5a2aec3f-e47b-44ab-b3da-2275578e9636",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array([-4., -4.]),\n",
-       " array([-1.2519118 ,  0.90778485]),\n",
-       " array([-2.02500166,  2.8935338 ])]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gd2_race.df_list_p1"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b89040ec-4318-419b-95eb-60320f146877",
+   "id": "d6001078-5cf2-402e-b37e-a6dad90674a6",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -117,7 +194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.13"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,

algorithm/optimization/.ipynb_checkpoints/gd-checkpoint.py

@@ -330,12 +330,16 @@ class gradient_descent_2d_race(object):
     def __init__(self, environ:str="jupyterlab"):
         pio.renderers.default = environ # 'notebook' or 'colab' or 'jupyterlab'
         self.wg_expr = widgets.Text(value="(sin(x1) - 2) ** 2 + (sin(x2) - 2) ** 2", description="Expression:")
-        self.wg_person_one = widgets.Text(value="(0, 0)", description="coordinate 1:")
+        self.wg_person_one = widgets.Text(value="(5, 5)", description="candidate 1:")
-        self.wg_person_two = widgets.Text(value="(0, 0)", description="coordinate 2:")
+        self.wg_person_two = widgets.Text(value="(5, 5)", description="candidate 2:")
         self.button_compute = widgets.Button(description="Compute")
         self.button_plot = widgets.Button(description="Plot")
+        
         self.compute_output = widgets.Output()
-        self.config = widgets.VBox([self.wg_expr, self.wg_person_one, self.wg_person_two, self.button_compute])
+        self.plot_output = widgets.Output()
+        
+        self.button_box = widgets.HBox([self.button_compute, self.button_plot], description="operations")
+        self.config = widgets.VBox([self.wg_expr, self.wg_person_one, self.wg_person_two, self.button_box])
         self.xn_list_p1, self.df_list_p1 = [], []
         self.xn_list_p2, self.df_list_p2 = [], []
         self.initialization()
@@ -344,8 +348,8 @@ class gradient_descent_2d_race(object):
         display(self.config) 
         self.button_compute.on_click(self.compute)
         display(self.compute_output)
-        #self.button_plot.on_click(self.plot)
+        self.button_plot.on_click(self.plot)
-        #display(self.plot_output)
+        display(self.plot_output)
     
     def compute(self, *args):
         with self.compute_output:
@@ -375,5 +379,60 @@ class gradient_descent_2d_race(object):
             print("player two: gradient= {}".format(gradient))
             clear_output(wait=True)
             return None
+
+    def plot(self, *args):
+        with self.plot_output:
+            clear_output(wait=True)
+            #x0 = np.array(self.wg_x0.value.split(","), dtype=float)
+            x1 = symbols("x1")
+            x2 = symbols("x2")
+            expr = sympify(self.wg_expr.value)
+            xx1 = np.arange(np.array(self.xn_list_p1)[:, 0].min()*0.5, np.array(self.xn_list_p1)[:, 0].max()*1.5, 0.1)
+            xx2 = np.arange(np.array(self.xn_list_p1)[:, 1].min()*0.5, np.array(self.xn_list_p1)[:, 1].max()*1.5, 0.1)
+            xx1, xx2 = np.meshgrid(xx1, xx2)
+
+            f = lambdify((x1, x2), expr, "numpy")
+            fx = f(xx1, xx2)
+            f_xn_p1 = f(np.array(self.xn_list_p1)[:, 0], np.array(self.xn_list_p1)[:, 1])
+            f_xn_p2 = f(np.array(self.xn_list_p2)[:, 0], np.array(self.xn_list_p2)[:, 1])
+            
+            frames, steps = [], []
+            for k in range(len(f_xn_p1)):
+                tmp_trace1 = go.Scatter3d(x=np.array(self.xn_list_p1)[:k,0], y=np.array(self.xn_list_p1)[:k,1], z=f_xn_p1)
+                tmp_trace2 = go.Scatter3d(x=np.array(self.xn_list_p2)[:k,0], y=np.array(self.xn_list_p2)[:k,1], z=f_xn_p2)
+                frame = go.Frame(dict(data=[tmp_trace1, tmp_trace2], name=f'frame{k+1}'), traces=[1, 2])
+                frames.append(frame)
+                step = dict(
+                    method="update",
+                    args=[{"visible": [True]},
+                          {"title": "Slider switched to step: " + str(k+1)}],  # layout attribute
+                )
+                steps.append(step)
+            
+            sliders = [dict(steps= [dict(method= 'animate',
+                                   args= [[f'frame{k+1}'],
+                                          dict(mode= 'immediate',
+                                          frame= dict( duration=0, redraw= True ),
+                                                   transition=dict( duration=0)
+                                                  )
+                                            ],
+                                    #label='Date : {}'.format(date_range[k])
+                                     ) for k in range(0,len(frames))], 
+                        transition= dict(duration=0),
+                        x=0,
+                        y=0,
+                        currentvalue=dict(font=dict(size=12), visible=True, xanchor= 'center'),
+                        len=1.0)
+                   ]
+
+            trace1 = go.Surface(x=xx1, y=xx2, z=fx, showscale=True, opacity=0.4)
+            trace2 = go.Scatter3d(x=None, y=None, z=None)
+            trace3 = go.Scatter3d(x=None, y=None, z=None)
+            fig = go.Figure(data=[trace1, trace2, trace3], frames=frames)
+            fig.update_layout(updatemenus=[dict(type="buttons", buttons=[dict(label="Play", method="animate", args=[None, dict(fromcurrent=True)]), \
+                                                                         dict(label="Pause", method="animate", args=[[None], dict(fromcurrent=True, mode='immediate', transition= {'duration': 0}, frame=dict(redraw=True, duration=0))])])],
+                             margin=dict(l=20, r=20, b=20, t=20), sliders=sliders)
+            fig.show()
+            
         
         

algorithm/optimization/gd.py

@@ -330,12 +330,16 @@ class gradient_descent_2d_race(object):
     def __init__(self, environ:str="jupyterlab"):
         pio.renderers.default = environ # 'notebook' or 'colab' or 'jupyterlab'
         self.wg_expr = widgets.Text(value="(sin(x1) - 2) ** 2 + (sin(x2) - 2) ** 2", description="Expression:")
-        self.wg_person_one = widgets.Text(value="(0, 0)", description="coordinate 1:")
+        self.wg_person_one = widgets.Text(value="(5, 5)", description="candidate 1:")
-        self.wg_person_two = widgets.Text(value="(0, 0)", description="coordinate 2:")
+        self.wg_person_two = widgets.Text(value="(5, 5)", description="candidate 2:")
         self.button_compute = widgets.Button(description="Compute")
         self.button_plot = widgets.Button(description="Plot")
+        
         self.compute_output = widgets.Output()
-        self.config = widgets.VBox([self.wg_expr, self.wg_person_one, self.wg_person_two, self.button_compute])
+        self.plot_output = widgets.Output()
+        
+        self.button_box = widgets.HBox([self.button_compute, self.button_plot], description="operations")
+        self.config = widgets.VBox([self.wg_expr, self.wg_person_one, self.wg_person_two, self.button_box])
         self.xn_list_p1, self.df_list_p1 = [], []
         self.xn_list_p2, self.df_list_p2 = [], []
         self.initialization()
@@ -344,8 +348,8 @@ class gradient_descent_2d_race(object):
         display(self.config) 
         self.button_compute.on_click(self.compute)
         display(self.compute_output)
-        #self.button_plot.on_click(self.plot)
+        self.button_plot.on_click(self.plot)
-        #display(self.plot_output)
+        display(self.plot_output)
     
     def compute(self, *args):
         with self.compute_output:
@@ -375,5 +379,60 @@ class gradient_descent_2d_race(object):
             print("player two: gradient= {}".format(gradient))
             clear_output(wait=True)
             return None
+
+    def plot(self, *args):
+        with self.plot_output:
+            clear_output(wait=True)
+            #x0 = np.array(self.wg_x0.value.split(","), dtype=float)
+            x1 = symbols("x1")
+            x2 = symbols("x2")
+            expr = sympify(self.wg_expr.value)
+            xx1 = np.arange(np.array(self.xn_list_p1)[:, 0].min()*0.5, np.array(self.xn_list_p1)[:, 0].max()*1.5, 0.1)
+            xx2 = np.arange(np.array(self.xn_list_p1)[:, 1].min()*0.5, np.array(self.xn_list_p1)[:, 1].max()*1.5, 0.1)
+            xx1, xx2 = np.meshgrid(xx1, xx2)
+
+            f = lambdify((x1, x2), expr, "numpy")
+            fx = f(xx1, xx2)
+            f_xn_p1 = f(np.array(self.xn_list_p1)[:, 0], np.array(self.xn_list_p1)[:, 1])
+            f_xn_p2 = f(np.array(self.xn_list_p2)[:, 0], np.array(self.xn_list_p2)[:, 1])
+            
+            frames, steps = [], []
+            for k in range(len(f_xn_p1)):
+                tmp_trace1 = go.Scatter3d(x=np.array(self.xn_list_p1)[:k,0], y=np.array(self.xn_list_p1)[:k,1], z=f_xn_p1)
+                tmp_trace2 = go.Scatter3d(x=np.array(self.xn_list_p2)[:k,0], y=np.array(self.xn_list_p2)[:k,1], z=f_xn_p2)
+                frame = go.Frame(dict(data=[tmp_trace1, tmp_trace2], name=f'frame{k+1}'), traces=[1, 2])
+                frames.append(frame)
+                step = dict(
+                    method="update",
+                    args=[{"visible": [True]},
+                          {"title": "Slider switched to step: " + str(k+1)}],  # layout attribute
+                )
+                steps.append(step)
+            
+            sliders = [dict(steps= [dict(method= 'animate',
+                                   args= [[f'frame{k+1}'],
+                                          dict(mode= 'immediate',
+                                          frame= dict( duration=0, redraw= True ),
+                                                   transition=dict( duration=0)
+                                                  )
+                                            ],
+                                    #label='Date : {}'.format(date_range[k])
+                                     ) for k in range(0,len(frames))], 
+                        transition= dict(duration=0),
+                        x=0,
+                        y=0,
+                        currentvalue=dict(font=dict(size=12), visible=True, xanchor= 'center'),
+                        len=1.0)
+                   ]
+
+            trace1 = go.Surface(x=xx1, y=xx2, z=fx, showscale=True, opacity=0.4)
+            trace2 = go.Scatter3d(x=None, y=None, z=None)
+            trace3 = go.Scatter3d(x=None, y=None, z=None)
+            fig = go.Figure(data=[trace1, trace2, trace3], frames=frames)
+            fig.update_layout(updatemenus=[dict(type="buttons", buttons=[dict(label="Play", method="animate", args=[None, dict(fromcurrent=True)]), \
+                                                                         dict(label="Pause", method="animate", args=[[None], dict(fromcurrent=True, mode='immediate', transition= {'duration': 0}, frame=dict(redraw=True, duration=0))])])],
+                             margin=dict(l=20, r=20, b=20, t=20), sliders=sliders)
+            fig.show()
+