fix merge conflict post hoc

emer · May 2, 2024 · 8f6f24a · 8f6f24a
1 parent 1ef5da0
commit 8f6f24a
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Showing 4 changed files with 10 additions and 12 deletions.
diff --git a/README.md b/README.md
@@ -43,11 +43,7 @@ See [python README](https://github.com/emer/leabra/blob/master/python/README.md)
 
 # Design / Organization
 
-<<<<<<< HEAD
 * The `emergent` repository contains a collection of packages supporting the implementation of biologically based neural networks.  The main package is `emer` which specifies a minimal abstract interface for a neural network.  The `etable` `etable.Table` data structure (DataTable in C++) is in a separate repository under the overall `emer` project umbrella, as are specific algorithms such as `leabra` which implement the `emer` interface.
-=======
-* The `emergent` repository contains a collection of packages supporting the implementation of biologically-based neural networks.  The main package is `emer` which specifies a minimal abstract interface for a neural network.  The `table` `table.Table` data structure (DataTable in C++) is in a separate repository under the overall `emer` project umbrella, as are specific algorithms such as `leabra` which implement the `emer` interface.
->>>>>>> 5874d12 (updated to core v0.1.2 with tensor/table etc)
 
 * Go uses `interfaces` to represent abstract collections of functionality (i.e., sets of methods).  The `emer` package provides a set of interfaces for each structural level (e.g., `emer.Layer` etc) -- any given specific layer must implement all of these methods, and the structural containers (e.g., the list of layers in a network) are lists of these interfaces.  An interface is implicitly a *pointer* to an actual concrete object that implements the interface.
 

diff --git a/egui/plots.go b/egui/plots.go
@@ -34,6 +34,7 @@ func (gui *GUI) AddPlots(title string, lg *elog.Logs) {
 
 		plt := gui.NewPlotTab(key, mode+" "+time+" Plot")
 		plt.SetTable(lt.Table)
+		plt.UpdatePlot()
 		plt.Params.FromMetaMap(lt.Meta)
 
 		ConfigPlotFromLog(title, plt, lg, key)

diff --git a/elog/README.md b/elog/README.md
@@ -209,7 +209,7 @@ Overall summary performance statistics have multiple Write functions for differe
             etime.Scope(etime.AllModes, etime.Trial): func(ctx *elog.Context) {
                 ctx.SetStatFloat("TrlUnitErr")
             }, etime.Scope(etime.AllModes, etime.Epoch): func(ctx *elog.Context) {
-                ctx.SetAgg(ctx.Mode, etime.Trial, agg.AggMean)
+                ctx.SetAgg(ctx.Mode, etime.Trial, stats.Mean)
             }, etime.Scope(etime.AllModes, etime.Run): func(ctx *elog.Context) {
                 ix := ctx.LastNRows(ctx.Mode, etime.Epoch, 5)
                 ctx.SetFloat64(agg.Mean(ix, ctx.Item.Name)[0])
@@ -251,7 +251,7 @@ Iterate over layers of interest (use `LayersByClass` function). It is *essential
             Type:   reflect.Float64,
             Plot:   false,
             FixMax: false,
-            Range:  minmax.F64{Max: 1},
+            Range:  minmax.F32{Max: 1},
             Write: elog.WriteMap{
                 etime.Scope(etime.Train, etime.Epoch): func(ctx *elog.Context) {
                     ly := ctx.Layer(clnm).(axon.AxonLayer).AsAxon()
@@ -268,13 +268,13 @@ Here's how to log a projection variable:
         Name:  clnm + "_FF_AvgMaxG",
         Type:  reflect.Float64,
         Plot:  false,
-        Range: minmax.F64{Max: 1},
+        Range: minmax.F32{Max: 1},
         Write: elog.WriteMap{
             etime.Scope(etime.Train, etime.Trial): func(ctx *elog.Context) {
                 ffpj := cly.RecvPrjn(0).(*axon.Prjn)
                 ctx.SetFloat32(ffpj.GScale.AvgMax)
             }, etime.Scope(etime.AllModes, etime.Epoch): func(ctx *elog.Context) {
-                ctx.SetAgg(ctx.Mode, etime.Trial, agg.AggMean)
+                ctx.SetAgg(ctx.Mode, etime.Trial, stats.Mean)
             }}})
 ```
 
@@ -293,7 +293,7 @@ A log column can be a tensor of any shape -- the `SetLayerTensor` method on the
             Type:      reflect.Float64,
             CellShape: cly.Shape().Shp,
             FixMax:    true,
-            Range:     minmax.F64{Max: 1},
+            Range:     minmax.F32{Max: 1},
             Write: elog.WriteMap{
                 etime.Scope(etime.Test, etime.Trial): func(ctx *elog.Context) {
                     ctx.SetLayerTensor(clnm, "Act")
@@ -326,7 +326,7 @@ Here's how you record the data and log the resulting stats, using the `Analyze`
             Type:      reflect.Float64,
             CellShape: cly.Shape().Shp,
             FixMax:    true,
-            Range:     minmax.F64{Max: 1},
+            Range:     minmax.F32{Max: 1},
             Write: elog.WriteMap{
                 etime.Scope(etime.Analyze, etime.Trial): func(ctx *elog.Context) {
                     ctx.SetLayerTensor(clnm, "ActM")
@@ -357,13 +357,13 @@ This item creates a tensor column that records the average error for each catego
         CellShape: []int{20},
         DimNames:  []string{"Cat"},
         Plot:      true,
-        Range:     minmax.F64{Min: 0},
+        Range:     minmax.F32{Min: 0},
         TensorIndex: -1, // plot all values
         Write: elog.WriteMap{
             etime.Scope(etime.Test, etime.Epoch): func(ctx *elog.Context) {
                 ix := ctx.Logs.IndexView(etime.Test, etime.Trial)
                 spl := split.GroupBy(ix, []string{"Cat"})
-                split.AggTry(spl, "Err", agg.AggMean)
+                split.AggTry(spl, "Err", stats.Mean)
                 cats := spl.AggsToTable(table.ColumnNameOnly)
                 ss.Logs.MiscTables[ctx.Item.Name] = cats
                 ctx.SetTensor(cats.Columns[1])

diff --git a/patgen/configpats.go b/patgen/configpats.go
@@ -14,6 +14,7 @@ import (
 
 // InitPats initiates patterns to be used in MixPats
 func InitPats(dt *table.Table, name, desc, inputName, outputName string, listSize, ySize, xSize, poolY, poolX int) {
+	dt.DeleteAll()
 	dt.SetMetaData("name", name)
 	dt.SetMetaData("desc", desc)
 	dt.AddStringColumn("Name")