diff --git a/examples/data_proc.ipynb b/examples/data_proc.ipynb index ccb3c28..b304db3 100644 --- a/examples/data_proc.ipynb +++ b/examples/data_proc.ipynb @@ -269,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -279,12 +279,34 @@ "┌ Info: Checking data\n", "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/solvers/solve.jl:29\n", "┌ Info: Starting discretize kernel...\n", - "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/bases/utils.jl:20\n" + "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/bases/utils.jl:20\n", + "┌ Info: Kernel was discretized successfully.\n", + "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/bases/utils.jl:32\n", + "┌ Info: Finding optimal alpha\n", + "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/solvers/solve.jl:35\n", + "┌ Info: Optimized successfully, alphas = ArgmaxOptim([0.000100006], [0.0001], [10.0], [0.1], Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.HagerZhang{Float64,Base.RefValue{Bool}},Nothing,Nothing,Optim.Flat}(LineSearches.InitialStatic{Float64}\n", + "│ alpha: Float64 1.0\n", + "│ scaled: Bool false\n", + "│ , LineSearches.HagerZhang{Float64,Base.RefValue{Bool}}\n", + "│ delta: Float64 0.1\n", + "│ sigma: Float64 0.9\n", + "│ alphamax: Float64 Inf\n", + "│ rho: Float64 5.0\n", + "│ epsilon: Float64 1.0e-6\n", + "│ gamma: Float64 0.66\n", + "│ linesearchmax: Int64 50\n", + "│ psi3: Float64 0.1\n", + "│ display: Int64 0\n", + "│ mayterminate: Base.RefValue{Bool}\n", + "│ , nothing, nothing, Optim.Flat())).\n", + "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/utils/find_optimal_alpha.jl:46\n", + "┌ Info: Optimal alpha found\n", + "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/solvers/solve.jl:37\n" ] }, { "data": { - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -293,12 +315,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "┌ Info: Kernel was discretized successfully.\n", - "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/bases/utils.jl:32\n", - "┌ Info: Finding optimal alpha\n", - "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/solvers/solve.jl:35\n", - "┌ Info: Optimal alpha found\n", - "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/solvers/solve.jl:37\n", "┌ Info: Starting solution\n", "└ @ Main.TurchinReg /Users/ta_nyan/Documents/diploma/StatReg.jl/src/solvers/solve.jl:54\n", "┌ Info: Solved analytically\n", @@ -309,7 +325,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "alpha = 1000.0\n" + "alpha = 0.0001000055774001702\n" ] } ], @@ -341,7 +357,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# As the most probable value of the parameter, we got $\\alpha \\approx 10^{-3}$. Let's use another parameter values and plot needed and measured functions." + "# As the most probable value of the parameter, we got $\\alpha \\approx 10^{-4}$. Let's use another parameter values and plot needed and measured functions." ] }, {