+ SHO

Mark-Kramer · Dec 2, 2024 · 0948dba · 0948dba
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diff --git a/.DS_Store b/.DS_Store
diff --git a/Readings/Kramer_2022.pdf b/Readings/Kramer_2022.pdf
diff --git a/Readings/Roopun_2008.pdf b/Readings/Roopun_2008.pdf
diff --git a/Readings/Spyropoulos_2022.pdf b/Readings/Spyropoulos_2022.pdf
diff --git a/SHO_Lab.ipynb b/SHO_Lab.ipynb
@@ -89,14 +89,6 @@
     "x,t   = damped_harmonic_oscillator(omega,beta,noise,T0);\n",
     "plt.plot(t,x);"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6cf952e-e134-444e-b596-48d443806204",
-   "metadata": {},
-   "source": [
-    "### "
-   ]
   }
  ],
  "metadata": {
@@ -115,7 +107,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,

diff --git a/Slides/Simple_Model_Lecture.pdf b/Slides/Simple_Model_Lecture.pdf
diff --git a/_extensions/.DS_Store b/_extensions/.DS_Store
diff --git a/docs/.DS_Store b/docs/.DS_Store
diff --git a/docs/Analyzing_Rhythms_Lab_1.html b/docs/Analyzing_Rhythms_Lab_1.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Analyzing_Rhythms_Lab_2a.html b/docs/Analyzing_Rhythms_Lab_2a.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Analyzing_Rhythms_Lab_2b.html b/docs/Analyzing_Rhythms_Lab_2b.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Analyzing_Rhythms_Lab_3.html b/docs/Analyzing_Rhythms_Lab_3.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Backpropagation.html b/docs/Backpropagation.html
@@ -2,7 +2,7 @@
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@@ -228,7 +228,7 @@
 }
 
 // Store cell data
-globalThis.qpyodideCellDetails = [{"code":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd","id":1,"options":{"out-width":"700px","fig-width":7,"autorun":"","label":"","classes":"","out-height":"","read-only":"false","comment":"","warning":"true","fig-height":5,"fig-cap":"","results":"markup","message":"true","context":"interactive","dpi":72,"output":"true"}},{"code":"df = pd.read_csv(\"https://raw.githubusercontent.com/Mark-Kramer/BU-MA665-MA666/master/Data/backpropagation_example_data.csv\")\n\n# Extract the variables from the loaded data\nin_true = np.array(df.iloc[:,0])  #Get the values associated with the first column of the dataframe\nout_true = np.array(df.iloc[:,1])  #Get the values associated with the second column of the dataframe","id":2,"options":{"out-width":"700px","fig-width":7,"autorun":"","label":"","classes":"","out-height":"","read-only":"false","comment":"","warning":"true","fig-height":5,"fig-cap":"","results":"markup","message":"true","context":"interactive","dpi":72,"output":"true"}},{"code":"print(np.transpose([in_true, out_true]))","id":3,"options":{"out-width":"700px","fig-width":7,"autorun":"","label":"","classes":"","out-height":"","read-only":"false","comment":"","warning":"true","fig-height":5,"fig-cap":"","results":"markup","message":"true","context":"interactive","dpi":72,"output":"true"}},{"code":"def sigmoid(x):\n    return 1/(1+np.exp(-x))     # Define the sigmoid anonymous function.\n\ndef feedforward(w, s0):         # Define feedforward solution.\n    # ... x1 = activity of first neuron,\n    # ... s1 = output of first neuron,\n    # ... x2 = activity of second neuron,\n    # ... s2 = output of second neuron,\n    # ... out = output of neural network.\n    return out,s1,s2","id":4,"options":{"out-width":"700px","fig-width":7,"autorun":"","label":"","classes":"","out-height":"","read-only":"false","comment":"","warning":"true","fig-height":5,"fig-cap":"","results":"markup","message":"true","context":"interactive","dpi":72,"output":"true"}},{"code":"w     = [0.5,0.5]                  # Choose initial values for the weights.\nalpha = 0.01                    # Set the learning constant.\n\nK = np.size(in_true);\nresults = np.zeros([K,3])        # Define a variable to hold the results of each iteration.    \n\nfor k in np.arange(K):\n    s0     = in_true[k]          # Define the input,\n    target = out_true[k]         # ... and the target output.\n    \n    #Calculate feedforward solution to get output.\n    \n    #Update the weights.\n    w0 = w[0]; w1 = w[1];\n    w[1] = \"SOMETHING\"\n    w[0] = \"SOMETHING\"\n    \n    # Save the results of this step. --------------------------------------\n    # Here we save the 3 weights, and the neural network output.\n    # results[k,:] = [w[0],w[1],  out]\n\n# Plot the NN weights and error during training \n# plt.clf()\n# plt.plot(results[:,1], label='w1')\n# plt.plot(results[:,0], label='w0')\n# plt.plot(results[:,2]-target, label='error')\n# plt.legend()                       #Include a legend,\n# plt.xlabel('Iteration number');    #... and axis label.\n\n# Print the NN weights\n# print(results[-1,0:2])","id":5,"options":{"out-width":"700px","fig-width":7,"autorun":"","label":"","classes":"","out-height":"","read-only":"false","comment":"","warning":"true","fig-height":5,"fig-cap":"","results":"markup","message":"true","context":"interactive","dpi":72,"output":"true"}}];
+globalThis.qpyodideCellDetails = [{"code":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd","id":1,"options":{"label":"","autorun":"","read-only":"false","out-width":"700px","fig-width":7,"message":"true","results":"markup","comment":"","output":"true","fig-cap":"","warning":"true","dpi":72,"classes":"","fig-height":5,"context":"interactive","out-height":""}},{"code":"df = pd.read_csv(\"https://raw.githubusercontent.com/Mark-Kramer/BU-MA665-MA666/master/Data/backpropagation_example_data.csv\")\n\n# Extract the variables from the loaded data\nin_true = np.array(df.iloc[:,0])  #Get the values associated with the first column of the dataframe\nout_true = np.array(df.iloc[:,1])  #Get the values associated with the second column of the dataframe","id":2,"options":{"label":"","autorun":"","read-only":"false","out-width":"700px","fig-width":7,"message":"true","results":"markup","comment":"","output":"true","fig-cap":"","warning":"true","dpi":72,"classes":"","fig-height":5,"context":"interactive","out-height":""}},{"code":"print(np.transpose([in_true, out_true]))","id":3,"options":{"label":"","autorun":"","read-only":"false","out-width":"700px","fig-width":7,"message":"true","results":"markup","comment":"","output":"true","fig-cap":"","warning":"true","dpi":72,"classes":"","fig-height":5,"context":"interactive","out-height":""}},{"code":"def sigmoid(x):\n    return 1/(1+np.exp(-x))     # Define the sigmoid anonymous function.\n\ndef feedforward(w, s0):         # Define feedforward solution.\n    # ... x1 = activity of first neuron,\n    # ... s1 = output of first neuron,\n    # ... x2 = activity of second neuron,\n    # ... s2 = output of second neuron,\n    # ... out = output of neural network.\n    return out,s1,s2","id":4,"options":{"label":"","autorun":"","read-only":"false","out-width":"700px","fig-width":7,"message":"true","results":"markup","comment":"","output":"true","fig-cap":"","warning":"true","dpi":72,"classes":"","fig-height":5,"context":"interactive","out-height":""}},{"code":"w     = [0.5,0.5]                  # Choose initial values for the weights.\nalpha = 0.01                    # Set the learning constant.\n\nK = np.size(in_true);\nresults = np.zeros([K,3])        # Define a variable to hold the results of each iteration.    \n\nfor k in np.arange(K):\n    s0     = in_true[k]          # Define the input,\n    target = out_true[k]         # ... and the target output.\n    \n    #Calculate feedforward solution to get output.\n    \n    #Update the weights.\n    w0 = w[0]; w1 = w[1];\n    w[1] = \"SOMETHING\"\n    w[0] = \"SOMETHING\"\n    \n    # Save the results of this step. --------------------------------------\n    # Here we save the 3 weights, and the neural network output.\n    # results[k,:] = [w[0],w[1],  out]\n\n# Plot the NN weights and error during training \n# plt.clf()\n# plt.plot(results[:,1], label='w1')\n# plt.plot(results[:,0], label='w0')\n# plt.plot(results[:,2]-target, label='error')\n# plt.legend()                       #Include a legend,\n# plt.xlabel('Iteration number');    #... and axis label.\n\n# Print the NN weights\n# print(results[-1,0:2])","id":5,"options":{"label":"","autorun":"","read-only":"false","out-width":"700px","fig-width":7,"message":"true","results":"markup","comment":"","output":"true","fig-cap":"","warning":"true","dpi":72,"classes":"","fig-height":5,"context":"interactive","out-height":""}}];
 
 
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diff --git a/docs/Bursting_Lab.html b/docs/Bursting_Lab.html
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diff --git a/docs/CFC_Lab.html b/docs/CFC_Lab.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Coherence_Lab_Part_1.html b/docs/Coherence_Lab_Part_1.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Coherence_Lab_Part_2.html b/docs/Coherence_Lab_Part_2.html
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diff --git a/docs/Coherence_Lab_Part_3.html b/docs/Coherence_Lab_Part_3.html
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diff --git a/docs/Gamma_Lab.html b/docs/Gamma_Lab.html
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